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We propose a new method for realistic human motion transfer using a generative adversarial network (GAN), which generates a motion video of a target character imitating actions of a source character, while maintaining high authenticity of the generated results. We tackle the problem by decoupling and recombining the posture information and appearance information of both the source and target characters. The innovation of our approach lies in the use of the projection of a reconstructed 3D human model as the condition of GAN to better maintain the structural integrity of transfer results in different poses. We further introduce a detail enhancement net to enhance the details of transfer results by exploiting the details in real source frames. Extensive experiments show that our approach yields better results both qualitatively and quantitatively than the state-of-the-art methods. | Human Motion Transfer with 3D Constraints and Detail Enhancement | 10,300 |
Existing physical cloth simulators suffer from expensive computation and difficulties in tuning mechanical parameters to get desired wrinkling behaviors. Data-driven methods provide an alternative solution. It typically synthesizes cloth animation at a much lower computational cost, and also creates wrinkling effects that highly resemble the much controllable training data. In this paper we propose a deep learning based method for synthesizing cloth animation with high resolution meshes. To do this we first create a dataset for training: a pair of low and high resolution meshes are simulated and their motions are synchronized. As a result the two meshes exhibit similar large-scale deformation but different small wrinkles. Each simulated mesh pair are then converted into a pair of low and high resolution "images" (a 2D array of samples), with each sample can be interpreted as any of three features: the displacement, the normal and the velocity. With these image pairs, we design a multi-feature super-resolution (MFSR) network that jointly train an upsampling synthesizer for the three features. The MFSR architecture consists of two key components: a sharing module that takes multiple features as input to learn low-level representations from corresponding super-resolution tasks simultaneously; and task-specific modules focusing on various high-level semantics. Frame-to-frame consistency is well maintained thanks to the proposed kinematics-based loss function. Our method achieves realistic results at high frame rates: 12-14 times faster than traditional physical simulation. We demonstrate the performance of our method with various experimental scenes, including a dressed character with sophisticated collisions. | Multi-feature super-resolution network for cloth wrinkle synthesis | 10,301 |
We propose Pulsar, an efficient sphere-based differentiable renderer that is orders of magnitude faster than competing techniques, modular, and easy-to-use due to its tight integration with PyTorch. Differentiable rendering is the foundation for modern neural rendering approaches, since it enables end-to-end training of 3D scene representations from image observations. However, gradient-based optimization of neural mesh, voxel, or function representations suffers from multiple challenges, i.e., topological inconsistencies, high memory footprints, or slow rendering speeds. To alleviate these problems, Pulsar employs: 1) a sphere-based scene representation, 2) an efficient differentiable rendering engine, and 3) neural shading. Pulsar executes orders of magnitude faster than existing techniques and allows real-time rendering and optimization of representations with millions of spheres. Using spheres for the scene representation, unprecedented speed is obtained while avoiding topology problems. Pulsar is fully differentiable and thus enables a plethora of applications, ranging from 3D reconstruction to general neural rendering. | Pulsar: Efficient Sphere-based Neural Rendering | 10,302 |
We propose a novel approach to extracting crack-free iso-surfaces from Structured AMR data that is more general than previous techniques, is trivially simple to implement, requires no information other than the list of AMR cells, and works, in particular, for different AMR formats including octree AMR, block-structured AMR with arbitrary level differences at level boundaries, and AMR data that consist of individual cells without any existing grid structure. We describe both the technique itself and a CUDA-based GPU implementation of this technique, and evaluate it on several non-trivial AMR data sets. | A Simple, General, and GPU Friendly Method for Computing Dual Mesh and
Iso-Surfaces of Adaptive Mesh Refinement (AMR) Data | 10,303 |
An intuitive design method is proposed for generating developable ruled B-spline surfaces from a sequence of straight line segments indicating the surface shape. The first and last line segments are enforced to be the head and tail ruling lines of the resulting surface while the interior lines are required to approximate rulings on the resulting surface as much as possible. This manner of developable surface design is conceptually similar to the popular way of the freeform curve and surface design in the CAD community, observing that a developable ruled surface is a single parameter family of straight lines. This new design mode of the developable surface also provides more flexibility than the widely employed way of developable surface design from two boundary curves of the surface. The problem is treated by numerical optimization methods with which a particular level of distance error is allowed. We thus provide an effective tool for creating surfaces with a high degree of developability when the input control rulings do not lie in exact developable surfaces. We consider this ability as the superiority over analytical methods in that it can deal with arbitrary design inputs and find practically useful results. | Developable B-spline surface generation from control rulings | 10,304 |
ImageJ/Fiji is a widely-used tool in the biomedical community for performing everyday image analysis tasks. However, its 3D viewer component (aptly named 3D Viewer) has become dated and is no longer actively maintained. We set out to create an alternative tool that not only brings modern concepts and APIs from computer graphics to ImageJ, but is designed to be robust to long-term, open-source development. To achieve this we divided the visualization logic into two parts: the rendering framework, scenery, and the user-facing application, sciview. In this paper we describe the development process and design decisions made, putting an emphasis on sustainable development, community building, and software engineering best practises. We highlight the motivation for the Java Virtual Machine (JVM) as a target platform for visualisation applications. We conclude by discussing the remaining milestones and strategy for long-term sustainability. | Tales from the Trenches: Developing sciview, a new 3D viewer for the
ImageJ community | 10,305 |
Storyline visualizations display the interactions of groups and entities and their development over time. Existing approaches have successfully adopted the general layout from hand-drawn illustrations to automatically create similar depictions. Ward Shelley is the author of several diagrammatic paintings that show the timeline of art-related subjects, such as Downtown Body, a history of art scenes. His drawings include many stylistic elements that are not covered by existing storyline visualizations, like links between entities, splits and merges of streams, and tags or labels to describe the individual elements. We present a visualization method that provides a visual mapping for the complex relationships in the data, creates a layout for their display, and adopts a similar styling of elements to imitate the artistic appeal of such illustrations. We compare our results to the original drawings and provide an open-source authoring tool prototype. | Organic Narrative Charts | 10,306 |
We introduce a new technique to visualize complex flowing phenomena by using concepts from shape analysis. Our approach uses techniques that examine the intrinsic geometry of manifolds through their heat kernel, to obtain representations of such manifolds that are isometry-invariant and multi-scale. These representations permit us to compute heat kernel signatures of each point on that manifold, and we can use these signatures as features for classification and segmentation that identify points that have similar structural properties. Our approach adapts heat kernel signatures to unsteady flows by formulating a notion of shape where pathlines are observations of a manifold living in a high-dimensional space. We use this space to compute and visualize heat kernel signatures associated with each pathline. Besides being able to capture the structural features of a pathline, heat kernel signatures allow the comparison of pathlines from different flow datasets through a shape matching pipeline. We demonstrate the analytic power of heat kernel signatures by comparing both (1) different timesteps from the same unsteady flow as well as (2) flow datasets taken from ensemble simulations with varying simulation parameters. Our analysis only requires the pathlines themselves, and thus it does not utilize the underlying vector field directly. We make minimal assumptions on the pathlines: while we assume they are sampled from a continuous, unsteady flow, our computations can tolerate pathlines that have varying density and potential unknown boundaries. We evaluate our approach through visualizations of a variety of two-dimensional unsteady flows. | Visualization of Unsteady Flow Using Heat Kernel Signatures | 10,307 |
We are witnessing a proliferation of textured 3D models captured from the real world with automatic photo-reconstruction tools. Digital 3D models of this class come with a unique set of characteristics and defects -- especially concerning their parametrization -- setting them starkly apart from 3D models originating from other, more traditional, sources. We study this class of 3D models by collecting a significant number of representatives and quantitatively evaluating their quality according to several metrics. These include a new invariant metric we design to assess the fragmentation of the UV map, one of the main weaknesses hindering the usability of these models. Our results back the widely shared notion that such models are not fit for direct use in downstream applications (such as videogames), and require challenging processing steps. Regrettably, existing automatic geometry processing tools are not always up to the task: for example, we verify that available tools for UV optimization often fail due mesh inconsistencies, geometric and topological noise, excessive resolution, or other factors; moreover, even when an output is produced, it is rarely a significant improvement over the input (according to the aforementioned measures). Therefore, we argue that further advancements are required specifically targeted at this class of models. Towards this goal, we share the models we collected in the form of a new public repository, Real-World Textured Things (RWTT), a benchmark to systematic field-test and compare algorithms. RWTT consists of 568 carefully selected textured 3D models representative of all the main modern off-the-shelf photo-reconstruction tools. The repository is available at http://texturedmesh.isti.cnr.it/ and is browsable by metadata collected during experiments, and comes with a tool, TexMetro, providing the same set of measures for generic UV mapped datasets. | Real-World Textured Things: a Repository of Textured Models Generated
with Modern Photo-Reconstruction Tools | 10,308 |
Engineering design research integrating artificial intelligence (AI) into computer-aided design (CAD) and computer-aided engineering (CAE) is actively being conducted. This study proposes a deep learning-based CAD/CAE framework in the conceptual design phase that automatically generates 3D CAD designs and evaluates their engineering performance. The proposed framework comprises seven stages: (1) 2D generative design, (2) dimensionality reduction, (3) design of experiment in latent space, (4) CAD automation, (5) CAE automation, (6) transfer learning, and (7) visualization and analysis. The proposed framework is demonstrated through a road wheel design case study and indicates that AI can be practically incorporated into an end-use product design project. Engineers and industrial designers can jointly review a large number of generated 3D CAD models by using this framework along with the engineering performance results estimated by AI and find conceptual design candidates for the subsequent detailed design stage. | Integrating Deep Learning into CAD/CAE System: Generative Design and
Evaluation of 3D Conceptual Wheel | 10,309 |
In this paper we propose an approach for computing multiple high-quality near-isometric dense correspondences between a pair of 3D shapes. Our method is fully automatic and does not rely on user-provided landmarks or descriptors. This allows us to analyze the full space of maps and extract multiple diverse and accurate solutions, rather than optimizing for a single optimal correspondence as done in most previous approaches. To achieve this, we propose a compact tree structure based on the spectral map representation for encoding and enumerating possible rough initializations, and a novel efficient approach for refining them to dense pointwise maps. This leads to a new method capable of both producing multiple high-quality correspondences across shapes and revealing the symmetry structure of a shape without a priori information. In addition, we demonstrate through extensive experiments that our method is robust and results in more accurate correspondences than state-of-the-art for shape matching and symmetry detection. | MapTree: Recovering Multiple Solutions in the Space of Maps | 10,310 |
3D human dance motion is a cooperative and elegant social movement. Unlike regular simple locomotion, it is challenging to synthesize artistic dance motions due to the irregularity, kinematic complexity and diversity. It requires the synthesized dance is realistic, diverse and controllable. In this paper, we propose a novel generative motion model based on temporal convolution and LSTM,TC-LSTM, to synthesize realistic and diverse dance motion. We introduce a unique control signal, dance melody line, to heighten controllability. Hence, our model, and its switch for control signals, promote a variety of applications: random dance synthesis, music-to-dance, user control, and more. Our experiments demonstrate that our model can synthesize artistic dance motion in various dance types. Compared with existing methods, our method achieved start-of-the-art results. | Towards 3D Dance Motion Synthesis and Control | 10,311 |
Curves play a fundamental role across computer graphics, physical simulation, and mathematical visualization, yet most tools for curve design do nothing to prevent crossings or self-intersections. This paper develops efficient algorithms for (self-)repulsion of plane and space curves that are well-suited to problems in computational design. Our starting point is the so-called tangent-point energy, which provides an infinite barrier to self-intersection. In contrast to local collision detection strategies used in, e.g., physical simulation, this energy considers interactions between all pairs of points, and is hence useful for global shape optimization: local minima tend to be aesthetically pleasing, physically valid, and nicely distributed in space. A reformulation of gradient descent, based on a Sobolev-Slobodeckij inner product enables us to make rapid progress toward local minima---independent of curve resolution. We also develop a hierarchical multigrid scheme that significantly reduces the per-step cost of optimization. The energy is easily integrated with a variety of constraints and penalties (e.g., inextensibility, or obstacle avoidance), which we use for applications including curve packing, knot untangling, graph embedding, non-crossing spline interpolation, flow visualization, and robotic path planning. | Repulsive Curves | 10,312 |
EMU is an efficient and scalable model to simulate bulk musculoskeletal motion with heterogenous materials. First, EMU requires no model reductions, or geometric coarsening, thereby producing results visually accurate when compared to an FEM simulation. Second, EMU is efficient and scales much better than state-of-the-art FEM with the number of elements in the mesh, and is more easily parallelizable. Third, EMU can handle heterogeneously stiff meshes with an arbitrary constitutive model, thus allowing it to simulate soft muscles, stiff tendons and even stiffer bones all within one unified system. These three key characteristics of EMU enable us to efficiently orchestrate muscle activated skeletal movements. We demonstrate the efficacy of our approach via a number of examples with tendons, muscles, bones and joints. | EMU: Efficient Muscle Simulation In Deformation Space | 10,313 |
Toolpath planning is an important task in laser aided additive manufacturing (LAAM) and other direct energy deposition (DED) processes. The deposition toolpaths for complex geometries with slender structures can be further optimized by partitioning the sliced 2D layers into sub-regions, and enable the design of appropriate infill toolpaths for different sub-regions. However, reported approaches for 2D layer segmentation generally require manual operations that are tedious and time-consuming. To increase segmentation efficiency, this paper proposes an autonomous approach based on evolutional computation for 2D layer segmentation. The algorithm works in an identify-and-segment manner. Specifically, the largest quasi-quadrilateral is identified and segmented from the target layer iteratively. Results from case studies have validated the effectiveness and efficacy of the developed algorithm. To further improve its performance, a roughing-finishing strategy is proposed. Via multi-processing, the strategy can remarkably increase the solution variety without affecting solution quality and search time, thus providing great application potential in LAAM toolpath planning. To the best of the authors knowledge, this work is the first to address automatic 2D layer segmentation problem in LAAM process. Therefore, it may be a valuable supplement to the state of the art in this area. | An Evolutional Algorithm for Automatic 2D Layer Segmentation in
Laser-aided Additive Manufacturing | 10,314 |
NVidia RTX platform has been changing and extending the possibilities for real time Computer Graphics applications. It is the first time in history that retail graphics cards have full hardware support for ray tracing primitives. It still a long way to fully understand and optimize its use and this task itself is a fertile field for scientific progression. However, another path is to explore the platform as an expansion of paradigms for other problems. For example, the integration of real time Ray Tracing and Virtual Reality can result in interesting applications for visualization of Non-Euclidean Geometry and 3D Manifolds. In this paper we present Ray-VR, a novel algorithm for real time stereo ray tracing, constructed on top of Falcor, NVidia's scientific prototyping framework. | Ray-VR: Ray Tracing Virtual Reality in Falcor | 10,315 |
Recently, there have been a lot of researches to synthesize / edit the motion of a single avatar in the virtual environment. However, there has not been so much work of simulating continuous interactions of multiple avatars such as fighting. In this paper, we propose a new method to generate a realistic fighting scene based on motion capture data. We propose a new algorithm called the temporal expansion approach which maps the continuous time action plan to a discrete causality space such that turn-based evaluation methods can be used. As a result, it is possible to use many mature algorithms available in strategy games such as the Minimax algorithm and $\alpha-\beta$ pruning. We also propose a method to generate and use an offense/defense table, which illustrates the spatial-temporal relationship of attacks and dodges, to incorporate tactical maneuvers of defense into the scene. Using our method, avatars will plan their strategies taking into account the reaction of the opponent. Fighting scenes with multiple avatars are generated to demonstrate the effectiveness of our algorithm. The proposed method can also be applied to other kinds of continuous activities that require strategy planning such as sport games. | Technical Note: Generating Realistic Fighting Scenes by Game Tree | 10,316 |
We present a new scale-free geometric modelling environment designed by the author of the paper. It allows one to consistently treat geometric objects of arbitrary size and offers extensive analytic and computational support for visualization of both real and artificial sceneries. | SN-Engine, a Scale-free Geometric Modelling Environment | 10,317 |
We propose an image warping-based remote rendering technique for volumes that decouples the rendering and display phases. Our work builds on prior work that samples the volume on the client using ray casting and reconstructs a z-value based on some heuristic. The color and depth buffer are then sent to the client that reuses this depth image as a stand-in for subsequent frames by warping it according to the current camera position until new data was received from the server. We augment that method by implementing the client renderer using ray tracing. By representing the pixel contributions as spheres, this allows us to effectively vary their footprint based on the distance to the viewer, which we find to give better results than point-based rasterization when applied to volumetric data sets. | Augmenting Image Warping-Based Remote Volume Rendering with Ray Tracing | 10,318 |
This paper presents a deep normal filtering network, called DNF-Net, for mesh denoising. To better capture local geometry, our network processes the mesh in terms of local patches extracted from the mesh. Overall, DNF-Net is an end-to-end network that takes patches of facet normals as inputs and directly outputs the corresponding denoised facet normals of the patches. In this way, we can reconstruct the geometry from the denoised normals with feature preservation. Besides the overall network architecture, our contributions include a novel multi-scale feature embedding unit, a residual learning strategy to remove noise, and a deeply-supervised joint loss function. Compared with the recent data-driven works on mesh denoising, DNF-Net does not require manual input to extract features and better utilizes the training data to enhance its denoising performance. Finally, we present comprehensive experiments to evaluate our method and demonstrate its superiority over the state of the art on both synthetic and real-scanned meshes. | DNF-Net: a Deep Normal Filtering Network for Mesh Denoising | 10,319 |
Edge bundling methods can effectively alleviate visual clutter and reveal high-level graph structures in large graph visualization. Researchers have devoted significant efforts to improve edge bundling according to different metrics. As the edge bundling family evolve rapidly, the quality of edge bundles receives increasing attention in the literature accordingly. In this paper, we present MLSEB, a novel method to generate edge bundles based on moving least squares (MLS) approximation. In comparison with previous edge bundling methods, we argue that our MLSEB approach can generate better results based on a quantitative metric of quality, and also ensure scalability and the efficiency for visualizing large graphs. | MLSEB: Edge Bundling using Moving Least Squares Approximation | 10,320 |
Example-based mesh deformation methods are powerful tools for realistic shape editing. However, existing techniques typically combine all the example deformation modes, which can lead to overfitting, i.e. using a overly complicated model to explain the user-specified deformation. This leads to implausible or unstable deformation results, including unexpected global changes outside the region of interest. To address this fundamental limitation, we propose a sparse blending method that automatically selects a smaller number of deformation modes to compactly describe the desired deformation. This along with a suitably chosen deformation basis including spatially localized deformation modes leads to significant advantages, including more meaningful, reliable, and efficient deformations because fewer and localized deformation modes are applied. To cope with large rotations, we develop a simple but effective representation based on polar decomposition of deformation gradients, which resolves the ambiguity of large global rotations using an as-consistent-as-possible global optimization. This simple representation has a closed form solution for derivatives, making it efficient for sparse localized representation and thus ensuring interactive performance. Experimental results show that our method outperforms state-of-the-art data-driven mesh deformation methods, for both quality of results and efficiency. | Sparse Data Driven Mesh Deformation | 10,321 |
In this paper, we address a novel problem of cloning a patch of the source spherical panoramic image to the target spherical panoramic image, which we call 360 panorama cloning. Considering the sphere geometry constraint embedded in spherical panoramic images, we develop a coordinate-based method that directly clones in the spherical domain. Our method neither differentiates the polar regions and equatorial regions, nor identifies the boundaries in the unrolled planar-formatted panorama. We discuss in depth two unique issues in panorama cloning, i.e. preserving the patch's orientation, and handling the large-patch cloning (covering over 180 field of view) which may suffer from discoloration artifacts. As experimental results demonstrate, our method is able to get visually pleasing cloning results and achieve real time cloning performance. | 360 Panorama Cloning on Sphere | 10,322 |
In this paper, we present a spectral graph wavelet approach for shape analysis of carpal bones of human wrist. We apply a metric called global spectral graph wavelet signature for representation of cortical surface of the carpal bone based on eigensystem of Laplace-Beltrami operator. Furthermore, we propose a heuristic and efficient way of aggregating local descriptors of a carpal bone surface to global descriptor. The resultant global descriptor is not only isometric invariant, but also much more efficient and requires less memory storage. We perform experiments on shape of the carpal bones of ten women and ten men from a publicly-available database. Experimental results show the excellency of the proposed GSGW compared to recent proposed GPS embedding approach for comparing shapes of the carpal bones across populations. | Global spectral graph wavelet signature for surface analysis of carpal
bones | 10,323 |
Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing. Several methods have recently been developed, with an aim to extract intuitive and interpretable deformation components. However, these techniques suffer from fundamental limitations especially for meshes with noise or large-scale deformations, and may not always be able to identify important deformation components. In this paper we propose a novel mesh-based autoencoder architecture that is able to cope with meshes with irregular topology. We introduce sparse regularization in this framework, which along with convolutional operations, helps localize deformations. Our framework is capable of extracting localized deformation components from mesh data sets with large-scale deformations and is robust to noise. It also provides a nonlinear approach to reconstruction of meshes using the extracted basis, which is more effective than the current linear combination approach. Extensive experiments show that our method outperforms state-of-the-art methods in both qualitative and quantitative evaluations. | Mesh-based Autoencoders for Localized Deformation Component Analysis | 10,324 |
3D geometric contents are becoming increasingly popular. In this paper, we study the problem of analyzing deforming 3D meshes using deep neural networks. Deforming 3D meshes are flexible to represent 3D animation sequences as well as collections of objects of the same category, allowing diverse shapes with large-scale non-linear deformations. We propose a novel framework which we call mesh variational autoencoders (mesh VAE), to explore the probabilistic latent space of 3D surfaces. The framework is easy to train, and requires very few training examples. We also propose an extended model which allows flexibly adjusting the significance of different latent variables by altering the prior distribution. Extensive experiments demonstrate that our general framework is able to learn a reasonable representation for a collection of deformable shapes, and produce competitive results for a variety of applications, including shape generation, shape interpolation, shape space embedding and shape exploration, outperforming state-of-the-art methods. | Variational Autoencoders for Deforming 3D Mesh Models | 10,325 |
This article describes a method of getting a harmonious combination of colors, developed by us on the basis of the relationship of color and acoustic waves. Presents a parallel between harmoniously matched colors and the concept of harmony in music theory (consonance). Describes the physical assumption of the essence of the phenomenon of harmony (consonance). The article also provides algorithm of implementation wave method for the sRGB color model. | The wave method of building color palette and its application in
computer graphics | 10,326 |
In this paper we present a novel representation for deformation fields of 3D shapes, by considering the induced changes in the underlying metric. In particular, our approach allows to represent a deformation field in a coordinate-free way as a linear operator acting on real-valued functions defined on the shape. Such a representation both provides a way to relate deformation fields to other classical functional operators and enables analysis and processing of deformation fields using standard linear-algebraic tools. This opens the door to a wide variety of applications such as explicitly adding extrinsic information into the computation of functional maps, intrinsic shape symmetrization, joint deformation design through precise control of metric distortion, and coordinate-free deformation transfer without requiring pointwise correspondences. Our method is applicable to both surface and volumetric shape representations and we guarantee the equivalence between the operator-based and standard deformation field representation under mild genericity conditions in the discrete setting. We demonstrate the utility of our approach by comparing it with existing techniques and show how our representation provides a powerful toolbox for a wide variety of challenging problems. | Functional Characterization of Deformation Fields | 10,327 |
Videos captured by consumer cameras often exhibit temporal variations in color and tone that are caused by camera auto-adjustments like white-balance and exposure. When such videos are sub-sampled to play fast-forward, as in the increasingly popular forms of timelapse and hyperlapse videos, these temporal variations are exacerbated and appear as visually disturbing high frequency flickering. Previous techniques to photometrically stabilize videos typically rely on computing dense correspondences between video frames, and use these correspondences to remove all color changes in the video sequences. However, this approach is limited in fast-forward videos that often have large content changes and also might exhibit changes in scene illumination that should be preserved. In this work, we propose a novel photometric stabilization algorithm for fast-forward videos that is robust to large content-variation across frames. We compute pairwise color and tone transformations between neighboring frames and smooth these pair-wise transformations while taking in account the possibility of scene/content variations. This allows us to eliminate high-frequency fluctuations, while still adapting to real variations in scene characteristics. We evaluate our technique on a new dataset consisting of controlled synthetic and real videos, and demonstrate that our techniques outperforms the state-of-the-art. | Photometric Stabilization for Fast-forward Videos | 10,328 |
We present the first algorithm for designing volumetric Michell Trusses. Our method uses a parametrization approach to generate trusses made of structural elements aligned with the primary direction of an object's stress field. Such trusses exhibit high strength-to-weight ratios. We demonstrate the structural robustness of our designs via a posteriori physical simulation. We believe our algorithm serves as an important complement to existing structural optimization tools and as a novel standalone design tool itself. | Designing Volumetric Truss Structures | 10,329 |
This tutorial describes the geometry and algorithms for generating line drawings from 3D models, focusing on occluding contours. The geometry of occluding contours on meshes and on smooth surfaces is described in detail, together with algorithms for extracting contours, computing their visibility, and creating stylized renderings and animations. Exact methods and hardware-accelerated fast methods are both described, and the trade-offs between different methods are discussed. The tutorial brings together and organizes material that, at present, is scattered throughout the literature. It also includes some novel explanations, and implementation tips. A thorough survey of the field of non-photorealistic 3D rendering is also included, covering other kinds of line drawings and artistic shading. | Line Drawings from 3D Models | 10,330 |
In this paper, we present a novel method for learning to synthesize 3D mesh animation sequences with long short-term memory (LSTM) blocks and mesh-based convolutional neural networks (CNNs). Synthesizing realistic 3D mesh animation sequences is a challenging and important task in computer animation. To achieve this, researchers have long been focusing on shape analysis to develop new interpolation and extrapolation techniques. However, such techniques have limited learning capabilities and therefore can produce unrealistic animation. Deep architectures that operate directly on mesh sequences remain unexplored, due to the following major barriers: meshes with irregular triangles, sequences containing rich temporal information and flexible deformations. To address these, we utilize convolutional neural networks defined on triangular meshes along with a shape deformation representation to extract useful features, followed by LSTM cells that iteratively process the features. To allow completion of a missing mesh sequence from given endpoints, we propose a new weight-shared bidirectional structure. The bidirectional generation loss also helps mitigate error accumulation over iterations. Benefiting from all these technical advances, our approach outperforms existing methods in sequence prediction and completion both qualitatively and quantitatively. Moreover, this network can also generate follow-up frames conditioned on initial shapes and improve the accuracy as more bootstrap models are provided, which other works in the geometry processing domain cannot achieve. | Learning Bidirectional LSTM Networks for Synthesizing 3D Mesh Animation
Sequences | 10,331 |
Seamless global parametrization of surfaces is a key operation in geometry processing, e.g. for high-quality quad mesh generation. A common approach is to prescribe the parametric domain structure, in particular the locations of parametrization singularities (cones), and solve a non-convex optimization problem minimizing a distortion measure, with local injectivity imposed through either constraints or barrier terms. In both cases, an initial valid parametrization is essential to serve as feasible starting point for obtaining an optimized solution. While convexified versions of the constraints eliminate this initialization requirement, they narrow the range of solutions, causing some problem instances that actually do have a solution to become infeasible. We demonstrate that for arbitrary given sets of topologically admissible parametric cones with prescribed curvature, a global seamless parametrization always exists (with the exception of one well-known case). Importantly, our proof is constructive and directly leads to a general algorithm for computing such parametrizations. Most distinctively, this algorithm is bootstrapped with a convex optimization problem (solving for a conformal map), in tandem with a simple linear equation system (determining a seamless modification of this map). This initial map can then serve as valid starting point and be optimized with respect to application specific distortion measures using existing injectivity preserving methods. | Seamless Parametrization with Arbitrarily Prescribed Cones | 10,332 |
We present a novel linear subdivision scheme for face-based tangent directional fields on triangle meshes. Our subdivision scheme is based on a novel coordinate-free representation of directional fields as halfedge-based scalar quantities, bridging the finite-element representation with discrete exterior calculus. By commuting with differential operators, our subdivision is structure-preserving: it reproduces curl-free fields precisely, and reproduces divergence-free fields in the weak sense. Moreover, our subdivision scheme directly extends to directional fields with several vectors per face by working on the branched covering space. Finally, we demonstrate how our scheme can be applied to directional-field design, advection, and robust earth mover's distance computation, for efficient and robust computation. | Subdivision Directional Fields | 10,333 |
We report empirical study results on the color encoding of ensemble scalar and orientation to visualize diffusion magnetic resonance imaging (DMRI) tubes. The experiment tested six scalar colormaps for average fractional anisotropy (FA) tasks (grayscale, blackbody, diverging, isoluminant-rainbow, extended-blackbody, and coolwarm) and four three-dimensional (3D) directional encodings for tract tracing tasks (uniform gray, absolute, eigenmap, and Boy's surface embedding). We found that extended-blackbody, coolwarm, and blackbody remain the best three approaches for identifying ensemble average in 3D. Isoluminant-rainbow coloring led to the same ensemble mean accuracy as other colormaps. However, more than 50% of the answers consistently had higher estimates of the ensemble average, independent of the mean values. Hue, not luminance, influences ensemble estimates of mean values. For ensemble orientation-tracing tasks, we found that the Boy's surface embedding (greatest spatial resolution and contrast) and absolute color (lowest spatial resolution and contrast) schemes led to more accurate answers than the eigenmaps scheme (medium resolution and contrast), acting as the uncanny-valley phenomenon of visualization design in terms of accuracy. | Measuring the Effects of Scalar and Spherical Colormaps on Ensembles of
DMRI Tubes | 10,334 |
We present a system for acquiring, processing, and rendering panoramic light field still photography for display in Virtual Reality (VR). We acquire spherical light field datasets with two novel light field camera rigs designed for portable and efficient light field acquisition. We introduce a novel real-time light field reconstruction algorithm that uses a per-view geometry and a disk-based blending field. We also demonstrate how to use a light field prefiltering operation to project from a high-quality offline reconstruction model into our real-time model while suppressing artifacts. We introduce a practical approach for compressing light fields by modifying the VP9 video codec to provide high quality compression with real-time, random access decompression. We combine these components into a complete light field system offering convenient acquisition, compact file size, and high-quality rendering while generating stereo views at 90Hz on commodity VR hardware. Using our system, we built a freely available light field experience application called Welcome to Light Fields featuring a library of panoramic light field stills for consumer VR which has been downloaded over 15,000 times. | A System for Acquiring, Processing, and Rendering Panoramic Light Field
Stills for Virtual Reality | 10,335 |
This work presents a novel framework for spherical mesh parameterization. An efficient angle-preserving spherical parameterization algorithm is introduced, which is based on dynamic Yamabe flow and the conformal welding method with solid theoretic foundation. An area-preserving spherical parameterization is also discussed, which is based on discrete optimal mass transport theory. Furthermore, a spherical parameterization algorithm, which is based on the polar decomposition method, balancing angle distortion and area distortion is presented. The algorithms are tested on 3D geometric data and the experiments demonstrate the efficiency and efficacy of the proposed methods. | Spherical Parameterization Balancing Angle and Area Distortions | 10,336 |
Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures. Yet, recovering spatially-varying bi-directional reflectance distribution functions (SVBRDFs) from a single image based on such cues has challenged researchers in computer graphics for decades. We tackle lightweight appearance capture by training a deep neural network to automatically extract and make sense of these visual cues. Once trained, our network is capable of recovering per-pixel normal, diffuse albedo, specular albedo and specular roughness from a single picture of a flat surface lit by a hand-held flash. We achieve this goal by introducing several innovations on training data acquisition and network design. For training, we leverage a large dataset of artist-created, procedural SVBRDFs which we sample and render under multiple lighting directions. We further amplify the data by material mixing to cover a wide diversity of shading effects, which allows our network to work across many material classes. Motivated by the observation that distant regions of a material sample often offer complementary visual cues, we design a network that combines an encoder-decoder convolutional track for local feature extraction with a fully-connected track for global feature extraction and propagation. Many important material effects are view-dependent, and as such ambiguous when observed in a single image. We tackle this challenge by defining the loss as a differentiable SVBRDF similarity metric that compares the renderings of the predicted maps against renderings of the ground truth from several lighting and viewing directions. Combined together, these novel ingredients bring clear improvement over state of the art methods for single-shot capture of spatially varying BRDFs. | Single-Image SVBRDF Capture with a Rendering-Aware Deep Network | 10,337 |
We introduce a lightweight structure optimization approach for problems in which there is uncertainty in the force locations. Such uncertainty may arise due to force contact locations that change during use or are simply unknown a priori. Given an input 3D model, regions on its boundary where arbitrary normal forces may make contact, and a total force-magnitude budget, our algorithm generates a minimum weight 3D structure that withstands any force configuration capped by the budget. Our approach works by repeatedly finding the most critical force configuration and altering the internal structure accordingly. A key issue, however, is that the critical force configuration changes as the structure evolves, resulting in a significant computational challenge. To address this, we propose an efficient critical instant analysis approach. Combined with a reduced order formulation, our method provides a practical solution to the structural optimization problem. We demonstrate our method on a variety of models and validate it with mechanical tests. | Lightweight Structure Design Under Force Location Uncertainty | 10,338 |
This paper proposes an approach to content-preserving stitching of images with regular boundary constraints, which aims to stitch multiple images to generate a panoramic image with regular boundary. Existing methods treat image stitching and rectangling as two separate steps, which may result in suboptimal results as the stitching process is not aware of the further warping needs for rectangling. We address these limitations by formulating image stitching with regular boundaries in a unified optimization. Starting from the initial stitching results produced by traditional warping-based optimization, we obtain the irregular boundary from the warped meshes by polygon Boolean operations which robustly handle arbitrary mesh compositions, and by analyzing the irregular boundary construct a piecewise rectangular boundary. Based on this, we further incorporate straight line preserving and regular boundary constraints into the image stitching framework, and conduct iterative optimization to obtain an optimal piecewise rectangular boundary, thus can make the panoramic boundary as close as possible to a rectangle, while reducing unwanted distortions. We further extend our method to panoramic videos and selfie photography, by integrating the temporal coherence and portrait preservation into the optimization. Experiments show that our method efficiently produces visually pleasing panoramas with regular boundaries and unnoticeable distortions. | Content-Preserving Image Stitching with Regular Boundary Constraints | 10,339 |
We propose the first GPU algorithm for the 3D triangulation refinement problem. For an input of a piecewise linear complex $\mathcal{G}$ and a constant $B$, it produces, by adding Steiner points, a constrained Delaunay triangulation conforming to $\mathcal{G}$ and containing tetrahedra mostly of radius-edge ratios smaller than $B$. Our implementation of the algorithm shows that it can be an order of magnitude faster than the best CPU algorithm while using a similar amount of Steiner points to produce triangulations of comparable quality. | Computing Three-dimensional Constrained Delaunay Refinement Using the
GPU | 10,340 |
Robust feature extraction is an integral part of scientific visualization. In unsteady vector field analysis, researchers recently directed their attention towards the computation of near-steady reference frames for vortex extraction, which is a numerically challenging endeavor. In this paper, we utilize a convolutional neural network to combine two steps of the visualization pipeline in an end-to-end manner: the filtering and the feature extraction. We use neural networks for the extraction of a steady reference frame for a given unsteady 2D vector field. By conditioning the neural network to noisy inputs and resampling artifacts, we obtain numerically stabler results than existing optimization-based approaches. Supervised deep learning typically requires a large amount of training data. Thus, our second contribution is the creation of a vector field benchmark data set, which is generally useful for any local deep learning-based feature extraction. Based on Vatistas velocity profile, we formulate a parametric vector field mixture model that we parameterize based on numerically-computed example vector fields in near-steady reference frames. Given the parametric model, we can efficiently synthesize thousands of vector fields that serve as input to our deep learning architecture. The proposed network is evaluated on an unseen numerical fluid flow simulation. | Robust Reference Frame Extraction from Unsteady 2D Vector Fields with
Convolutional Neural Networks | 10,341 |
We present a new fully automatic block-decomposition hexahedral meshing algorithm capable of producing high quality meshes that strictly preserve feature curve networks on the input surface and align with an input surface cross-field. We produce all-hex meshes on the vast majority of inputs, and introduce localized non-hex elements only when the surface feature network necessitates those. The input to our framework is a closed surface with a collection of geometric or user-demarcated feature curves and a feature-aligned surface cross-field. Its output is a compact set of blocks whose edges interpolate these features and are loosely aligned with this cross-field. We obtain this block decomposition by cutting the input model using a collection of simple cutting surfaces bounded by closed surface loops. The set of cutting loops spans the input feature curves, ensuring feature preservation, and is obtained using a field-space sampling process. The computed loops are uniformly distributed across the surface, cross orthogonally, and are loosely aligned with the cross-field directions, inducing the desired block decomposition. We validate our method by applying it to a large range of complex inputs and comparing our results to those produced by state-of-the-art alternatives. Contrary to prior approaches, our framework consistently produces high-quality field aligned meshes while strictly preserving geometric or user-specified surface features. | Loopy Cuts: Surface-Field Aware Block Decomposition for Hex-Meshing | 10,342 |
We propose a modification to Perlin noise which use computable hash functions instead of textures as lookup tables. We implemented the FNV1, Jenkins and Murmur hashes on Shader Model 4.0 Graphics Processing Units for noise generation. Modified versions of the FNV1 and Jenkins hashes provide very close performance compared to a texture based Perlin noise implementation. Our noise modification enables noise function evaluation without any texture fetches, trading computational power for memory bandwidth. | Implementing Noise with Hash functions for Graphics Processing Units | 10,343 |
We present a novel method for reconstructing parametric, volumetric, multi-story building models from unstructured, unfiltered indoor point clouds by means of solving an integer linear optimization problem. Our approach overcomes limitations of previous methods in several ways: First, we drop assumptions about the input data such as the availability of separate scans as an initial room segmentation. Instead, a fully automatic room segmentation and outlier removal is performed on the unstructured point clouds. Second, restricting the solution space of our optimization approach to arrangements of volumetric wall entities representing the structure of a building enforces a consistent model of volumetric, interconnected walls fitted to the observed data instead of unconnected, paper-thin surfaces. Third, we formulate the optimization as an integer linear programming problem which allows for an exact solution instead of the approximations achieved with most previous techniques. Lastly, our optimization approach is designed to incorporate hard constraints which were difficult or even impossible to integrate before. We evaluate and demonstrate the capabilities of our proposed approach on a variety of complex real-world point clouds. | Automatic reconstruction of fully volumetric 3D building models from
point clouds | 10,344 |
We present a computational design system that assists users to model, optimize, and fabricate quad-robots with soft skins.Our system addresses the challenging task of predicting their physical behavior by fully integrating the multibody dynamics of the mechanical skeleton and the elastic behavior of the soft skin. The developed motion control strategy uses an alternating optimization scheme to avoid expensive full space time-optimization, interleaving space-time optimization for the skeleton and frame-by-frame optimization for the full dynamics. The output are motor torques to drive the robot to achieve a user prescribed motion trajectory.We also provide a collection of convenient engineering tools and empirical manufacturing guidance to support the fabrication of the designed quad-robot. We validate the feasibility of designs generated with our system through physics simulations and with a physically-fabricated prototype. | Computational Design of Skinned Quad-Robots | 10,345 |
This paper presents a novel technique for progressive online integration of uncalibrated image sequences with substantial geometric and/or photometric discrepancies into a single, geometrically and photometrically consistent image. Our approach can handle large sets of images, acquired from a nearly planar or infinitely distant scene at different resolutions in object domain and under variable local or global illumination conditions. It allows for efficient user guidance as its progressive nature provides a valid and consistent reconstruction at any moment during the online refinement process. Our approach avoids global optimization techniques, as commonly used in the field of image refinement, and progressively incorporates new imagery into a dynamically extendable and memory-efficient Laplacian pyramid. Our image registration process includes a coarse homography and a local refinement stage using optical flow. Photometric consistency is achieved by retaining the photometric intensities given in a reference image, while it is being refined. Globally blurred imagery and local geometric inconsistencies due to, e.g. motion are detected and removed prior to image fusion. We demonstrate the quality and robustness of our approach using several image and video sequences, including handheld acquisition with mobile phones and zooming sequences with consumer cameras. | Progressive Refinement Imaging | 10,346 |
We propose a data reduction technique for scattered data based on statistical sampling. Our void-and-cluster sampling technique finds a representative subset that is optimally distributed in the spatial domain with respect to the blue noise property. In addition, it can adapt to a given density function, which we use to sample regions of high complexity in the multivariate value domain more densely. Moreover, our sampling technique implicitly defines an ordering on the samples that enables progressive data loading and a continuous level-of-detail representation. We extend our technique to sample time-dependent trajectories, for example pathlines in a time interval, using an efficient and iterative approach. Furthermore, we introduce a local and continuous error measure to quantify how well a set of samples represents the original dataset. We apply this error measure during sampling to guide the number of samples that are taken. Finally, we use this error measure and other quantities to evaluate the quality, performance, and scalability of our algorithm. | Void-and-Cluster Sampling of Large Scattered Data and Trajectories | 10,347 |
In character animation, direction invariance is a desirable property. That is, a pose facing north and the same pose facing south are considered the same; a character that can walk to the north is expected to be able to walk to the south in a similar style. To achieve such direction invariance, the current practice is to remove the facing direction's rotation around the vertical axis before further processing. Such a scheme, however, is not robust for rotational behaviors in the sagittal plane. In search of a smooth scheme to achieve direction invariance, we prove that in general a singularity free scheme does not exist. We further connect the problem with the hairy ball theorem, which is better-known to the graphics community. Due to the nonexistence of a singularity free scheme, a general solution does not exist and we propose a remedy by using a properly-chosen motion direction that can avoid singularities for specific motions at hand. We perform comparative studies using two deep-learning based methods, one builds kinematic motion representations and the other learns physics-based controls. The results show that with our robust direction invariant features, both methods can achieve better results in terms of learning speed and/or final quality. We hope this paper can not only boost performance for character animation methods, but also help related communities currently not fully aware of the direction invariance problem to achieve more robust results. | Towards Robust Direction Invariance in Character Animation | 10,348 |
LightGuider is a novel guidance-based approach to interactive lighting design, which typically consists of interleaved 3D modeling operations and light transport simulations. Rather than having designers use a trial-and-error approach to match their illumination constraints and aesthetic goals, LightGuider supports the process by simulating potential next modeling steps that can deliver the most significant improvements. LightGuider takes predefined quality criteria and the current focus of the designer into account to visualize suggestions for lighting-design improvements via a specialized provenance tree. This provenance tree integrates snapshot visualizations of how well a design meets the given quality criteria weighted by the designer's preferences. This integration facilitates the analysis of quality improvements over the course of a modeling workflow as well as the comparison of alternative design solutions. We evaluate our approach with three lighting designers to illustrate its usefulness. | LightGuider: Guiding Interactive Lighting Design using Suggestions,
Provenance, and Quality Visualization | 10,349 |
This paper introduces a deep neural network based method, i.e., DeepOrganNet, to generate and visualize high-fidelity 3D / 4D organ geometric models from single-view medical image in real time. Traditional 3D / 4D medical image reconstruction requires near hundreds of projections, which cost insufferable computational time and deliver undesirable high imaging / radiation dose to human subjects. Moreover, it always needs further notorious processes to extract the accurate 3D organ models subsequently. To our knowledge, there is no method directly and explicitly reconstructing multiple 3D organ meshes from a single 2D medical grayscale image on the fly. Given single-view 2D medical images, e.g., 3D / 4D-CT projections or X-ray images, our end-to-end DeepOrganNet framework can efficiently and effectively reconstruct 3D / 4D lung models with a variety of geometric shapes by learning the smooth deformation fields from multiple templates based on a trivariate tensor-product deformation technique, leveraging an informative latent descriptor extracted from input 2D images. The proposed method can guarantee to generate high-quality and high-fidelity manifold meshes for 3D / 4D lung models. The major contributions of this work are to accurately reconstruct the 3D organ shapes from 2D single-view projection, significantly improve the procedure time to allow on-the-fly visualization, and dramatically reduce the imaging dose for human subjects. Experimental results are evaluated and compared with the traditional reconstruction method and the state-of-the-art in deep learning, by using extensive 3D and 4D examples from synthetic phantom and real patient datasets. The proposed method only needs several milliseconds to generate organ meshes with 10K vertices, which has a great potential to be used in real-time image guided radiation therapy (IGRT). | DeepOrganNet: On-the-Fly Reconstruction and Visualization of 3D / 4D
Lung Models from Single-View Projections by Deep Deformation Network | 10,350 |
Computer animation in conjunction with 3D printing has the potential to positively impact traditional stop-motion animation. As 3D printing every frame of a computer animation is prohibitively slow and expensive, 3D printed stop-motion can only be viable if animations can be faithfully reproduced using a compact library of 3D printed and efficiently assemblable parts. We thus present the first system for processing computer animation sequences (typically faces) to produce an optimal set of replacement parts for use in 3D printed stop-motion animation. Given an input animation sequence of topology invariant deforming meshes, our problem is to output a library of replacement parts and per-animation-frame assignment of the parts, such that we maximally approximate the input animation, while minimizing the amount of 3D printing and assembly. Inspired by current stop-motion workflows, a user manually indicates which parts of the model are preferred for segmentation; then, we find curves with minimal deformation along which to segment the mesh. We then present a novel algorithm to zero out deformations along the segment boundaries, so that replacement sets for each part can be interchangeably and seamlessly assembled together. The part boundaries are designed to ease 3D printing and instrumentation for assembly. Each part is then independently optimized using a graph-cut technique to find a set of replacements, whose size can be user defined, or automatically computed to adhere to a printing budget or allowed deviation from the original animation. Our evaluation is threefold: we show results on a variety of facial animations, both digital and 3D printed, critiqued by a professional animator; we show the impact of various algorithmic parameters; and compare our results to naive solutions. Our approach can reduce the printing time and cost significantly for stop-motion animated films. | A system for efficient 3D printed stop-motion face animation | 10,351 |
In this paper, we propose a perceptually-guided visualization sharpening technique. We analyze the spectral behavior of an established comprehensive perceptual model to arrive at our approximated model based on an adapted weighting of the bandpass images from a Gaussian pyramid. The main benefit of this approximated model is its controllability and predictability for sharpening color-mapped visualizations. Our method can be integrated into any visualization tool as it adopts generic image-based post-processing, and it is intuitive and easy to use as viewing distance is the only parameter. Using highly diverse datasets, we show the usefulness of our method across a wide range of typical visualizations. | Spectral Visualization Sharpening | 10,352 |
Facial animation is one of the most challenging problems in computer graphics, and it is often solved using linear heuristics like blend-shape rigging. More expressive approaches like physical simulation have emerged, but these methods are very difficult to tune, especially when simulating a real actor's face. We propose to use a simple finite element simulation approach for face animation, and present a novel method for recovering the required simulation parameters in order to best match a real actor's face motion. Our method involves reconstructing a very small number of head poses of the actor in 3D, where the head poses span different configurations of force directions due to gravity. Our algorithm can then automatically recover both the gravity-free rest shape of the face as well as the spatially-varying physical material stiffness such that a forward simulation will match the captured targets as closely as possible. As a result, our system can produce actor-specific, physical parameters that can be immediately used in recent physical simulation methods for faces. Furthermore, as the simulation results depend heavily on the chosen spatial layout of material clusters, we analyze and compare different spatial layouts. | Data-Driven Physical Face Inversion | 10,353 |
3D acquisition of archaeological artefacts has become an essential part of cultural heritage research for preservation or restoration purpose. Statues, in particular, have been at the center of many projects. In this paper, we introduce a way to improve the understanding of acquired statues representing real or imaginary creatures by registering a simple and pliable articulated model to the raw point set data. Our approach performs a Forward And bacKward Iterative Registration (FAKIR) which proceeds joint by joint, needing only a few iterations to converge. We are thus able to detect the pose and elementary anatomy of sculptures, with possibly non realistic body proportions. By adapting our simple skeleton, our method can work on animals and imaginary creatures. | FAKIR: An algorithm for revealing the anatomy and pose of statues from
raw point sets | 10,354 |
Generalized Pythagoras trees were developed for visualizing hierarchical data, producing organic, fractal-like representations. However, the drawback of the original layout algorithm is visual overlap of tree branches. To avoid such overlap, we introduce an adapted drawing algorithm using ellipses instead of circles to recursively place tree nodes representing the subhierarchies. Our technique is demonstrated by resolving overlap in diverse real-world and generated datasets, while comparing the results to the original approach. | Overlap-free Drawing of Generalized Pythagoras Trees for Hierarchy
Visualization | 10,355 |
Radial charts are generally considered less effective than linear charts. Perhaps the only exception is in visualizing periodical time-dependent data, which is believed to be naturally supported by the radial layout. It has been demonstrated that the drawbacks of radial charts outweigh the benefits of this natural mapping. Visualization of daily patterns, as a special case, has not been systematically evaluated using radial charts. In contrast to yearly or weekly recurrent trends, the analysis of daily patterns on a radial chart may benefit from our trained skill on reading radial clocks that are ubiquitous in our culture. In a crowd-sourced experiment with 92 non-expert users, we evaluated the accuracy, efficiency, and subjective ratings of radial and linear charts for visualizing daily traffic accident patterns. We systematically compared juxtaposed 12-hours variants and single 24-hours variants for both layouts in four low-level tasks and one high-level interpretation task. Our results show that over all tasks, the most elementary 24-hours linear bar chart is most accurate and efficient and is also preferred by the users. This provides strong evidence for the use of linear layouts - even for visualizing periodical daily patterns. | A Comparison of Radial and Linear Charts for Visualizing Daily Pattern | 10,356 |
We address the problem to infer physical material parameters and boundary conditions from the observed motion of a homogeneous deformable object via the solution of an inverse problem. Parameters are estimated from potentially unreliable real-world data sources such as sparse observations without correspondences. We introduce a novel Lagrangian-Eulerian optimization formulation, including a cost function that penalizes differences to observations during an optimization run. This formulation matches correspondence-free, sparse observations from a single-view depth sequence with a finite element simulation of deformable bodies. In conjunction with an efficient hexahedral discretization and a stable, implicit formulation of collisions, our method can be used in demanding situation to recover a variety of material parameters, ranging from Young's modulus and Poisson ratio to gravity and stiffness damping, and even external boundaries. In a number of tests using synthetic datasets and real-world measurements, we analyse the robustness of our approach and the convergence behavior of the numerical optimization scheme. | Sparse Surface Constraints for Combining Physics-based Elasticity
Simulation and Correspondence-Free Object Reconstruction | 10,357 |
Rendering realistic images with global illumination is a computationally demanding task and often requires dedicated hardware for feasible runtime. Recent research uses Deep Neural Networks to predict indirect lighting on image level, but such methods are commonly limited to diffuse materials and require training on each scene.We present Deep Radiance Caching (DRC), an efficient variant of Radiance Caching utilizing Convolutional Autoencoders for rendering global illumination. DRC employs a denoising neural network with Radiance Caching to support a wide range of material types, without the requirement of offline pre-computation or training for each scene.This offers high performance CPU rendering for maximum accessibility. Our method has been evaluated on interior scenes, and is able to produce high-quality images within 180 seconds on a single CPU. | Deep Radiance Caching: Convolutional Autoencoders Deeper in Ray Tracing | 10,358 |
We present a 3D stylization algorithm that can turn an input shape into the style of a cube while maintaining the content of the original shape. The key insight is that cubic style sculptures can be captured by the as-rigid-as-possible energy with an l1-regularization on rotated surface normals. Minimizing this energy naturally leads to a detail-preserving, cubic geometry. Our optimization can be solved efficiently without any mesh surgery. Our method serves as a non-realistic modeling tool where one can incorporate many artistic controls to create stylized geometries. | Cubic Stylization | 10,359 |
In this article we revisit the concept of abstraction as it is used in visualization and put it on a solid formal footing. While the term \emph{abstraction} is utilized in many scientific disciplines, arts, as well as everyday life, visualization inherits the notion of data abstraction or class abstraction from computer science, topological abstraction from mathematics, and visual abstraction from arts. All these notions have a lot in common, yet there is a major discrepancy in the terminology and basic understanding about visual abstraction in the context of visualization. We thus root the notion of abstraction in the philosophy of science, clarify the basic terminology, and provide crisp definitions of visual abstraction as a process. Furthermore, we clarify how it relates to similar terms often used interchangeably in the field of visualization. Visual abstraction is characterized by a conceptual space where this process exists, by the purpose it should serve, and by the perceptual and cognitive qualities of the beholder. These characteristics can be used to control the process of visual abstraction to produce effective and informative visual representations. | Visual Abstraction | 10,360 |
Three-dimensional (3D) printing is a powerful development tool both in industry, as well as in biomedical research. Additive-lathe 3D printing is an emerging sub-class of 3D printing whereby material is layered outward from the surface of a rotating cylindrical mandrel. While established additive manufacturing technologies have developed robust toolpath generation software, additive-lathe publications to date have been relegated to the most basic of proof-of-concept structures. This paper details the theory and implementation of a method for slicing a triangulated surface with a series of concentric, open, right circular cylinders that represents a crucial step in creating toolpaths to print complex models with additive-lathe technology. Valid edge cases are detailed which must be addressed when implementing a cylindrical slicer to produce non-intersecting closed contours; two classes of resultant closed contour are described. Methodologies for generating infill patterns, support structures and other considerations for toolpath construction are required prior to full implementation of a machine capable of printing complex geometry from a digital model onto a rotating cylindrical surface. This work represents the first thorough examination of the mathematics and algorithmic implementation of triangle mesh slicing with concentric cylinders and offers insights for future works in toolpath generation for the additive-lathe type 3D printer. | Triangle Mesh Slicing and Contour Construction for Three-Dimensional
Printing on a Rotating Mandrel | 10,361 |
We propose a method leveraging the naturally time-related expressivity of our voice to control an animation composed of a set of short events. The user records itself mimicking onomatopoeia sounds such as "Tick", "Pop", or "Chhh" which are associated with specific animation events. The recorded soundtrack is automatically analyzed to extract every instant and types of sounds. We finally synthesize an animation where each event type and timing correspond with the soundtrack. In addition to being a natural way to control animation timing, we demonstrate that multiple stories can be efficiently generated by recording different voice sequences. Also, the use of more than one soundtrack allows us to control different characters with overlapping actions. | Animation Synthesis Triggered by Vocal Mimics | 10,362 |
Motivated by the fact that the medial axis transform is able to encode nearly the complete shape, we propose to use as few medial balls as possible to approximate the original enclosed volume by the boundary surface. We progressively select new medial balls, in a top-down style, to enlarge the region spanned by the existing medial balls. The key spirit of the selection strategy is to encourage large medial balls while imposing given geometric constraints. We further propose a speedup technique based on a provable observation that the intersection of medial balls implies the adjacency of power cells (in the sense of the power crust). We further elaborate the selection rules in combination with two closely related applications. One application is to develop an easy-to-use ball-stick modeling system that helps non-professional users to quickly build a shape with only balls and wires, but any penetration between two medial balls must be suppressed. The other application is to generate porous structures with convex, compact (with a high isoperimetric quotient) and shape-aware pores where two adjacent spherical pores may have penetration as long as the mechanical rigidity can be well preserved. | Top-Down Shape Abstraction Based on Greedy Pole Selection | 10,363 |
Simulating turbulent smoke flows is computationally intensive due to their intrinsic multiscale behavior, thus requiring relatively high resolution grids to fully capture their complexity. For iterative editing or simply faster generation of smoke flows, dynamic upsampling of an input low-resolution numerical simulation is an attractive, yet currently unattainable goal. In this paper, we propose a novel dictionary-based learning approach to the dynamic upsampling of smoke flows. For each frame of an input coarse animation, we seek a sparse representation of small, local velocity patches of the flow based on an over-complete dictionary, and use the resulting sparse coefficients to generate a high-resolution smoke animation sequence. We propose a novel dictionary-based neural network which learns both a fast evaluation of sparse patch encoding and a dictionary of corresponding coarse and fine patches from a sequence of example simulations computed with any numerical solver. Our upsampling network then injects into coarse input sequences physics-driven fine details, unlike most previous approaches that only employed fast procedural models to add high frequency to the input. We present a variety of upsampling results for smoke flows and offer comparisons to their corresponding high-resolution simulations to demonstrate the effectiveness of our approach. | Dynamic Upsampling of Smoke through Dictionary-based Learning | 10,364 |
Projected augmented reality, also called projection mapping or video mapping, is a form of augmented reality that uses projected light to directly augment 3D surfaces, as opposed to using pass-through screens or headsets. The value of projected AR is its ability to add a layer of digital content directly onto physical objects or environments in a way that can be instantaneously viewed by multiple people, unencumbered by a screen or additional setup. Because projected AR typically involves projecting onto non-flat, textured objects (especially those that are conventionally not used as projection surfaces), the digital content needs to be mapped and aligned to precisely fit the physical scene to ensure a compelling experience. Current projected AR techniques require extensive calibration at the time of installation, which is not conducive to iteration or change, whether intentional (the scene is reconfigured) or not (the projector is bumped or settles). The workflows are undefined and fragmented, thus making it confusing and difficult for many to approach projected AR. For example, a digital artist may have the software expertise to create AR content, but could not complete an installation without experience in mounting, blending, and realigning projector(s); the converse is true for many A/V installation teams/professionals. Projection mapping has therefore been limited to high-end event productions, concerts, and films, because it requires expensive, complex tools, and skilled teams ($100K+ budgets). Lightform provides a technology that makes projected AR approachable, practical, intelligent, and robust through integrated hardware and computer-vision software. Lightform brings together and unites a currently fragmented workflow into a single cohesive process that provides users with an approachable and robust method to create and control projected AR experiences. | Lightform: Procedural Effects for Projected AR | 10,365 |
New algorithmic and hardware developments over the past two decades have enabled interactive ray tracing of small to modest sized scenes, and are finding growing popularity in scientific visualization and games. However, interactive ray tracing has not been as widely explored in the context of production film rendering, where challenges due to the complexity of the models and, from a practical standpoint, their unavailability to the wider research community, have posed significant challenges. The recent release of the Disney Moana Island Scene has made one such model available to the community for experimentation. In this paper, we detail the challenges posed by this scene to an interactive ray tracer, and the solutions we have employed and developed to enable interactive path tracing of the scene with full geometric and shading detail, with the goal of providing insight and guidance to other researchers. | Digesting the Elephant -- Experiences with Interactive Production
Quality Path Tracing of the Moana Island Scene | 10,366 |
With the introduction of Nvidia RTX hardware, ray tracing is now viable as a general real time rendering technique for complex 3D scenes. Leveraging this new technology, we present Raygun, an open source rendering, simulation, and game engine focusing on simplicity, expandability, and the topic of ray tracing realized through Nvidia's Vulkan ray tracing extension. | Running on Raygun | 10,367 |
Stroking and filling are the two basic rendering operations on paths in vector graphics. The theory of filling a path is well-understood in terms of contour integrals and winding numbers, but when path rendering standards specify stroking, they resort to the analogy of painting pixels with a brush that traces the outline of the path. This means important standards such as PDF, SVG, and PostScript lack a rigorous way to say what samples are inside or outside a stroked path. Our work fills this gap with a principled theory of stroking. Guided by our theory, we develop a novel polar stroking method to render stroked paths robustly with an intuitive way to bound the tessellation error without needing recursion. Because polar stroking guarantees small uniform steps in tangent angle, it provides an efficient way to accumulate arc length along a path for texturing or dashing. While this paper focuses on developing the theory of our polar stroking method, we have successfully implemented our methods on modern programmable GPUs. | Polar Stroking: New Theory and Methods for Stroking Paths | 10,368 |
We explain geometrically why ordinary facet angles of a stroked path tessellated from uniform tangent angle steps are bounded by twice the step angle. This fact means---excluding a small number of extraordinary facet angles straddling offset cusps---our polar stroking method bounds the facet angle size to less than $2 \theta$ where $\theta$ is the tangent step angle. | Ordinary Facet Angles of a Stroked Path Tessellated by Uniform Tangent
Angle Steps Are Bounded by Twice the Step Angle | 10,369 |
Dense flow visualization is a popular visualization paradigm. Traditionally, the various models and methods in this area use a continuous formulation, resting upon the solid foundation of functional analysis. In this work, we examine a discrete formulation of dense flow visualization. From probability theory, we derive a similarity matrix that measures the similarity between different points in the flow domain, leading to the discovery of a whole new class of visualization models. Using this matrix, we propose a novel visualization approach consisting of the computation of spectral embeddings, i.e., characteristic domain maps, defined by particle mixture probabilities. These embeddings are scalar fields that give insight into the mixing processes of the flow on different scales. The approach of spectral embeddings is already well studied in image segmentation, and we see that spectral embeddings are connected to Fourier expansions and frequencies. We showcase the utility of our method using different 2D and 3D flows. | A Discrete Probabilistic Approach to Dense Flow Visualization | 10,370 |
In this work, we present a novel, integrated rigged character simulation framework in Conformal Geometric Algebra (CGA) that supports, for the first time, real-time cuts and tears, before and/or after the animation, while maintaining deformation topology. The purpose of using CGA is to lift several restrictions posed by current state-of-the-art character animation & deformation methods. Previous implementations originally required weighted matrices to perform deformations, whereas, in the current state-of-the-art, dual-quaternions handle both rotations and translations, but cannot handle dilations. CGA is a suitable extension of dual-quaternion algebra that amends these two major previous shortcomings: the need to constantly transmute between matrices and dual-quaternions as well as the inability to properly dilate a model during animation. Our CGA algorithm also provides easy interpolation and application of all deformations in each intermediate steps, all within the same geometric framework. Furthermore we also present two novel algorithms that enable cutting and tearing of the input rigged, animated model, while the output model can be further re-deformed. These interactive, real-time cut and tear operations can enable a new suite of applications, especially under the scope of a medical surgical simulation. | Deform, Cut and Tear a skinned model using Conformal Geometric Algebra | 10,371 |
We present a method for designing smooth cross fields on surfaces that automatically align to sharp features of an underlying geometry. Our approach introduces a novel class of energies based on a representation of cross fields in the spherical harmonic basis. We provide theoretical analysis of these energies in the smooth setting, showing that they penalize deviations from surface creases while otherwise promoting intrinsically smooth fields. We demonstrate the applicability of our method to quad-meshing and include an extensive benchmark comparing our fields to other automatic approaches for generating feature-aligned cross fields on triangle meshes. | Octahedral Frames for Feature-Aligned Cross-Fields | 10,372 |
In this paper, we show that many complex patterns, which characterize the decorative style of many artisanal objects, can be generated by the recursive application of only four operators. Each operator is derived from tracing the isolines or the integral curves of geodesics fields generated from selected seeds on the surface. Based on this formulation, we present an interactive application that lets designers model complex recursive patterns directly on the object surface, without relying on parametrization. We support interaction on commodity hardware on meshes of a few million triangles, by combining light data structures together with an efficient approximate graph-based geodesic solver. We validate our approach by matching decoration styles from real-world photos, by analyzing the speed and accuracy of our geodesic solver, and by validating the interface with a user study. | DecoSurf: Recursive Geodesic Patterns on Triangle Meshes | 10,373 |
Insect swarms are common phenomena in nature and therefore have been actively pursued in computer animation. Realistic insect swarm simulation is difficult due to two challenges: high-fidelity behaviors and large scales, which make the simulation practice subject to laborious manual work and excessive trial-and-error processes. To address both challenges, we present a novel data-driven framework, FASTSWARM, to model complex behaviors of flying insects based on real-world data and simulate plausible animations of flying insect swarms. FASTSWARM has a linear time complexity and achieves real-time performance for large swarms. The high-fidelity behavior model of FASTSWARM explicitly takes into consideration the most common behaviors of flying insects, including the interactions among insects such as repulsion and attraction, the self-propelled behaviors such as target following and obstacle avoidance, and other characteristics such as the random movements. To achieve scalability, an energy minimization problem is formed with different behaviors modelled as energy terms, where the minimizer is the desired behavior. The minimizer is computed from the real-world data, which ensures the plausibility of the simulation results. Extensive simulation results and evaluations show that FASTSWARM is versatile in simulating various swarm behaviors, high fidelity measured by various metrics, easily controllable in inducing user controls and highly scalable. | FASTSWARM: A Data-driven FrAmework for Real-time Flying InSecT SWARM
Simulation | 10,374 |
In this paper, we propose a new construction for the Mexican hat wavelets on shapes with applications to partial shape matching. Our approach takes its main inspiration from the well-established methodology of diffusion wavelets. This novel construction allows us to rapidly compute a multiscale family of Mexican hat wavelet functions, by approximating the derivative of the heat kernel. We demonstrate that it leads to a family of functions that inherit many attractive properties of the heat kernel (e.g., a local support, ability to recover isometries from a single point, efficient computation). Due to its natural ability to encode high-frequency details on a shape, the proposed method reconstructs and transfers $\delta$-functions more accurately than the Laplace-Beltrami eigenfunction basis and other related bases. Finally, we apply our method to the challenging problems of partial and large-scale shape matching. An extensive comparison to the state-of-the-art shows that it is comparable in performance, while both simpler and much faster than competing approaches. | Wavelet-based Heat Kernel Derivatives: Towards Informative Localized
Shape Analysis | 10,375 |
Global Illumination (GI) is of utmost importance in the field of photo-realistic rendering. However, its computation has always been very complex, especially diffuse GI. State of the art real-time GI methods have limitations of different nature, such as light leaking, performance issues, special hardware requirements, noise corruption, bounce number limitations, among others. To overcome these limitations, we propose a novel approach of computing dynamic diffuse GI with a signed distance fields approximation of the scene and discretizing the space domain of the irradiance function. With this approach, we are able to estimate real-time diffuse GI for dynamic lighting and geometry, without any precomputations and supporting multi-bounce GI, providing good quality lighting and high performance at the same time. Our algorithm is also able to achieve better scalability, and manage both large open scenes and indoor high-detailed scenes without being corrupted by noise. | Signed Distance Fields Dynamic Diffuse Global Illumination | 10,376 |
We present a novel retargeting algorithm that transfers the musculature of a reference anatomical model to new bodies with different sizes, body proportions, muscle capability, and joint range of motion while preserving the functionality of the original musculature as closely as possible. The geometric configuration and physiological parameters of musculotendon units are estimated and optimized to adapt to new bodies. The range of motion around joints is estimated from a motion capture dataset and edited further for individual models. The retargeted model is simulation-ready, so we can physically simulate muscle-actuated motor skills with the model. Our system is capable of generating a wide variety of anatomical bodies that can be simulated to walk, run, jump and dance while maintaining balance under gravity. We will also demonstrate the construction of individualized musculoskeletal models from bi-planar X-ray images and medical examinations. | Functionality-Driven Musculature Retargeting | 10,377 |
We present a novel parallel algorithm for cloth simulation that exploits multiple GPUs for fast computation and the handling of very high resolution meshes. To accelerate implicit integration, we describe new parallel algorithms for sparse matrix-vector multiplication (SpMV) and for dynamic matrix assembly on a multi-GPU workstation. Our algorithms use a novel work queue generation scheme for a fat-tree GPU interconnect topology. Furthermore, we present a novel collision handling scheme that uses spatial hashing for discrete and continuous collision detection along with a non-linear impact zone solver. Our parallel schemes can distribute the computation and storage overhead among multiple GPUs and enable us to perform almost interactive simulation on complex cloth meshes, which can hardly be handled on a single GPU due to memory limitations. We have evaluated the performance with two multi-GPU workstations (with 4 and 8 GPUs, respectively) on cloth meshes with 0.5-1.65M triangles. Our approach can reliably handle the collisions and generate vivid wrinkles and folds at 2-5 fps, which is significantly faster than prior cloth simulation systems. We observe almost linear speedups with respect to the number of GPUs. | P-Cloth: Interactive Complex Cloth Simulation on Multi-GPU Systems using
Dynamic Matrix Assembly and Pipelined Implicit Integrators | 10,378 |
We give a method for modeling solid objects undergoing large spatially varying and/or anisotropic strains, and use it to reconstruct human anatomy from medical images. Our novel shape deformation method uses plastic strains and the Finite Element Method to successfully model shapes undergoing large and/or anisotropic strains, specified by sparse point constraints on the boundary of the object. We extensively compare our method to standard second-order shape deformation methods, variational methods and surface-based methods and demonstrate that our method avoids the spikiness, wiggliness and other artefacts of previous methods. We demonstrate how to perform such shape deformation both for attached and un-attached ("free flying") objects, using a novel method to solve linear systems with singular matrices with a known nullspace. While our method is applicable to general large-strain shape deformation modeling, we use it to create personalized 3D triangle and volumetric meshes of human organs, based on MRI or CT scans. Given a medically accurate anatomy template of a generic individual, we optimize the geometry of the organ to match the MRI or CT scan of a specific individual. Our examples include human hand muscles, a liver, a hip bone, and a gluteus medius muscle ("hip abductor"). | Modeling of Personalized Anatomy using Plastic Strains | 10,379 |
Virtual Reality (VR) enabled applications are becoming very important to visualize the terrain features in 3D. In general 3D datasets generated from high-resolution satellites and DEM occupy large volumes of data. However, lightweight datasets are required to create better user experiences on VR platforms. So, the present study develops a methodology to generate datasets compatible with VR using Indian Remote Sensing satellite (IRS) sensors. A Linear Imaging Self-Scanning System - IV (LISS IV) with 5.8 m spatial resolution and Carto DEM are used for generating the 3D view using the Arc environment and then converted into virtual reality modeling language (VRML) format. In order to reduce the volume of the VRML dataset a quadratic edge collapse decimation method is applied which reduces the number of faces in the mesh while preserving the boundary and/or normal. A granitic terrain in the south-west part of Hyderabad comprising of dyke intrusion is considered for the generation of 3D VR dataset, as it has high elevation differences thus rendering it most suitable for the present study. Further, the enhanced geomorphological features such as hills and valleys, geological structures such as fractures, intrusive (dykes) are studied and found suitable for better interpretation. | Mesh Processing Strategies and Fractals for Three Dimensional
Morphological Analysis of a Granitic Terrain using IRS LISS IV and Carto DEM | 10,380 |
Feature-preserving mesh denoising has received noticeable attention in visual media, with the aim of recovering high-fidelity, clean mesh shapes from the ones that are contaminated by noise. Existing denoising methods often design smaller weights for anisotropic surfaces and larger weights for isotropic surfaces in order to preserve sharp features, such as edges or corners, on the mesh shapes. However, they often disregard the fact that such small weights on anisotropic surfaces still pose negative impacts on the denoising outcomes and detail preservation results on the shapes. In this paper, we propose a novel segmentation-driven mesh denoising method which performs region-wise denoising, and thus avoids the disturbance of anisotropic neighbour faces for better feature preservation results. Also, our backbone can be easily embedded into commonly-used mesh denoising frameworks. Extensive experiments have demonstrated that our method can enhance the denoising results on a wide range of synthetic and real mesh models, both quantitatively and visually. | Segmentation-Driven Feature-Preserving Mesh Denoising | 10,381 |
We present a method for the efficient processing of contact and collision in volumetric elastic models simulated using the Projective Dynamics paradigm. Our approach enables interactive simulation of tetrahedral meshes with more than half a million elements, provided that the model satisfies two fundamental properties: the region of the model's surface that is susceptible to collision events needs to be known in advance, and the simulation degrees of freedom associated with that surface region should be limited to a small fraction (e.g. 5\%) of the total simulation nodes. Despite this conscious delineation of scope, our hypotheses hold true for common animation subjects, such as simulated models of the human face and parts of the body. In such scenarios, a partial Cholesky factorization can abstract away the behavior of the collision-safe subset of the face into the Schur Complement matrix with respect to the collision-prone region. We demonstrate how fast and accurate updates of penalty-based collision terms can be incorporated into this representation, and solved with high efficiency on the GPU. We also demonstrate the opportunity to iterate a partial update of the element rotations, akin to a selective application of the local step, specifically on the smaller collision-prone region without explicitly paying the cost associated with the rest of the simulation mesh. We demonstrate efficient and robust interactive simulation in detailed models from animation and medical applications. | Optimized Processing of Localized Collisions in Projective Dynamics | 10,382 |
Creating fine garment details requires significant efforts and huge computational resources. In contrast, a coarse shape may be easy to acquire in many scenarios (e.g., via low-resolution physically-based simulation, linear blend skinning driven by skeletal motion, portable scanners). In this paper, we show how to enhance, in a data-driven manner, rich yet plausible details starting from a coarse garment geometry. Once the parameterization of the garment is given, we formulate the task as a style transfer problem over the space of associated normal maps. In order to facilitate generalization across garment types and character motions, we introduce a patch-based formulation, that produces high-resolution details by matching a Gram matrix based style loss, to hallucinate geometric details (i.e., wrinkle density and shape). We extensively evaluate our method on a variety of production scenarios and show that our method is simple, light-weight, efficient, and generalizes across underlying garment types, sewing patterns, and body motion. | Deep Detail Enhancement for Any Garment | 10,383 |
The analysis of vector fields is crucial for the understanding of several physical phenomena, such as natural events (e.g., analysis of waves), diffusive processes, electric and electromagnetic fields. While previous work has been focused mainly on the analysis of 2D or 3D vector fields on volumes or surfaces, we address the meshless analysis of a vector field defined on an arbitrary domain, without assumptions on its dimension and discretisation. The meshless approximation of the Helmholtz-Hodge decomposition of a vector field is achieved by expressing the potential of its components as a linear combination of radial basis functions and by computing the corresponding conservative, irrotational, and harmonic components as solution to a least-squares or to a differential problem. To this end, we identify the conditions on the kernel of the radial basis functions that guarantee the existence of their derivatives. Finally, we demonstrate our approach on 2D and 3D vector fields measured by sensors or generated through simulation. | Meshless Approximation and Helmholtz-Hodge Decomposition of Vector
Fields | 10,384 |
We present a system that converts annotated broadcast video of tennis matches into interactively controllable video sprites that behave and appear like professional tennis players. Our approach is based on controllable video textures, and utilizes domain knowledge of the cyclic structure of tennis rallies to place clip transitions and accept control inputs at key decision-making moments of point play. Most importantly, we use points from the video collection to model a player's court positioning and shot selection decisions during points. We use these behavioral models to select video clips that reflect actions the real-life player is likely to take in a given match play situation, yielding sprites that behave realistically at the macro level of full points, not just individual tennis motions. Our system can generate novel points between professional tennis players that resemble Wimbledon broadcasts, enabling new experiences such as the creation of matchups between players that have not competed in real life, or interactive control of players in the Wimbledon final. According to expert tennis players, the rallies generated using our approach are significantly more realistic in terms of player behavior than video sprite methods that only consider the quality of motion transitions during video synthesis. | Vid2Player: Controllable Video Sprites that Behave and Appear like
Professional Tennis Players | 10,385 |
Volume rendering always plays an important role in the field of medical imaging and industrial design. In recent years, the realistic and interactive volume rendering of the global illumination can improve the perception of shape and depth of volumetric datasets. In this paper, a novel and flexible performance method of slice-based ray casting is proposed to implement the volume illumination effects, such as volume shadow and other scattering effects. This benefits from the slice-based illumination attenuation buffers of the whole geometry slices at the viewpoint of the light source and the high-efficiency shadow or scattering coefficient calculation per sample in ray casting. These tests show the method can obtain much better volume illumination effects and more scalable performance in contrast to the local volume illumination in ray casting volume rendering or other similar slice-based global volume illumination. | Interactive volume illumination of slice-based ray casting | 10,386 |
We present an unbiased numerical integration algorithm that handles both low-frequency regions and high frequency details of multidimensional integrals. It combines quadrature and Monte Carlo integration, by using a quadrature-base approximation as a control variate of the signal. We adaptively build the control variate constructed as a piecewise polynomial, which can be analytically integrated, and accurately reconstructs the low frequency regions of the integrand. We then recover the high-frequency details missed by the control variate by using Monte Carlo integration of the residual. Our work leverages importance sampling techniques by working in primary space, allowing the combination of multiple mappings; this enables multiple importance sampling in quadrature-based integration. Our algorithm is generic, and can be applied to any complex multidimensional integral. We demonstrate its effectiveness with four applications with low dimensionality: transmittance estimation in heterogeneous participating media, low-order scattering in homogeneous media, direct illumination computation, and rendering of distributed effects. Finally, we show how our technique is extensible to integrands of higher dimensionality, by computing the control variate on Monte Carlo estimates of the high-dimensional signal, and accounting for such additional dimensionality on the residual as well. In all cases, we show accurate results and faster convergence compared to previous approaches. | Primary-Space Adaptive Control Variates using Piecewise-Polynomial
Approximations | 10,387 |
Rapidly growing data sizes of scientific simulations pose significant challenges for interactive visualization and analysis techniques. In this work, we propose a compact probabilistic representation to interactively visualize large scattered datasets. In contrast to previous approaches that represent blocks of volumetric data using probability distributions, we model clusters of arbitrarily structured multivariate data. In detail, we discuss how to efficiently represent and store a high-dimensional distribution for each cluster. We observe that it suffices to consider low-dimensional marginal distributions for two or three data dimensions at a time to employ common visual analysis techniques. Based on this observation, we represent high-dimensional distributions by combinations of low-dimensional Gaussian mixture models. We discuss the application of common interactive visual analysis techniques to this representation. In particular, we investigate several frequency-based views, such as density plots in 1D and 2D, density-based parallel coordinates, and a time histogram. We visualize the uncertainty introduced by the representation, discuss a level-of-detail mechanism, and explicitly visualize outliers. Furthermore, we propose a spatial visualization by splatting anisotropic 3D Gaussians for which we derive a closed-form solution. Lastly, we describe the application of brushing and linking to this clustered representation. Our evaluation on several large, real-world datasets demonstrates the scaling of our approach. | Visual Analysis of Large Multivariate Scattered Data using Clustering
and Probabilistic Summaries | 10,388 |
Realistic speech-driven 3D facial animation is a challenging problem due to the complex relationship between speech and face. In this paper, we propose a deep architecture, called Geometry-guided Dense Perspective Network (GDPnet), to achieve speaker-independent realistic 3D facial animation. The encoder is designed with dense connections to strengthen feature propagation and encourage the re-use of audio features, and the decoder is integrated with an attention mechanism to adaptively recalibrate point-wise feature responses by explicitly modeling interdependencies between different neuron units. We also introduce a non-linear face reconstruction representation as a guidance of latent space to obtain more accurate deformation, which helps solve the geometry-related deformation and is good for generalization across subjects. Huber and HSIC (Hilbert-Schmidt Independence Criterion) constraints are adopted to promote the robustness of our model and to better exploit the non-linear and high-order correlations. Experimental results on the public dataset and real scanned dataset validate the superiority of our proposed GDPnet compared with state-of-the-art model. | Geometry-guided Dense Perspective Network for Speech-Driven Facial
Animation | 10,389 |
Physically based rendering is a discipline in computer graphics which aims at reproducing certain light and material appearances that occur in the real world. Complex scenes can be difficult to compute for rendering algorithms. This paper introduces a new comprehensive test database of scenes that treat different light setups in conjunction with diverse materials and discusses its design principles. A lot of research is focused on the development of new algorithms that can deal with difficult light conditions and materials efficiently. This database delivers a comprehensive foundation for evaluating existing and newly developed rendering techniques. A final evaluation compares different results of different rendering algorithms for all scenes. | Test Scene Design for Physically Based Rendering | 10,390 |
We present an assistive system for clipart design by providing visual scaffolds from the unseen viewpoints. Inspired by the artists' creation process, our system constructs the visual scaffold by first synthesizing the reference 3D shape of the input clipart and rendering it from the desired viewpoint. The critical challenge of constructing this visual scaffold is to generate a reference 3Dshape that matches the user's expectation in terms of object sizing and positioning while preserving the geometric style of the input clipart. To address this challenge, we propose a user-assisted curve extrusion method to obtain the reference 3D shape.We render the synthesized reference 3D shape with consistent style into the visual scaffold. By following the generated visual scaffold, the users can efficiently design clipart with their desired viewpoints. The user study conducted by an intuitive user interface and our generated visual scaffold suggests that the users are able to design clipart from different viewpoints while preserving the original geometric style without losing its original shape. | ClipFlip : Multi-view Clipart Design | 10,391 |
In the modern drug discovery process, medicinal chemists deal with the complexity of analysis of large ensembles of candidate molecules. Computational tools, such as dimensionality reduction (DR) and classification, are commonly used to efficiently process the multidimensional space of features. These underlying calculations often hinder interpretability of results and prevent experts from assessing the impact of individual molecular features on the resulting representations. To provide a solution for scrutinizing such complex data, we introduce ChemVA, an interactive application for the visual exploration of large molecular ensembles and their features. Our tool consists of multiple coordinated views: Hexagonal view, Detail view, 3D view, Table view, and a newly proposed Difference view designed for the comparison of DR projections. These views display DR projections combined with biological activity, selected molecular features, and confidence scores for each of these projections. This conjunction of views allows the user to drill down through the dataset and to efficiently select candidate compounds. Our approach was evaluated on two case studies of finding structurally similar ligands with similar binding affinity to a target protein, as well as on an external qualitative evaluation. The results suggest that our system allows effective visual inspection and comparison of different high-dimensional molecular representations. Furthermore, ChemVA assists in the identification of candidate compounds while providing information on the certainty behind different molecular representations. | ChemVA: Interactive Visual Analysis of Chemical Compound Similarity in
Virtual Screening | 10,392 |
We present a nonparametric statistical framework for the quantification, analysis, and propagation of data uncertainty in direct volume rendering (DVR). The state-of-the-art statistical DVR framework allows for preserving the transfer function (TF) of the ground truth function when visualizing uncertain data; however, the existing framework is restricted to parametric models of uncertainty. In this paper, we address the limitations of the existing DVR framework by extending the DVR framework for nonparametric distributions. We exploit the quantile interpolation technique to derive probability distributions representing uncertainty in viewing-ray sample intensities in closed form, which allows for accurate and efficient computation. We evaluate our proposed nonparametric statistical models through qualitative and quantitative comparisons with the mean-field and parametric statistical models, such as uniform and Gaussian, as well as Gaussian mixtures. In addition, we present an extension of the state-of-the-art rendering parametric framework to 2D TFs for improved DVR classifications. We show the applicability of our uncertainty quantification framework to ensemble, downsampled, and bivariate versions of scalar field datasets. | Direct Volume Rendering with Nonparametric Models of Uncertainty | 10,393 |
Representing complex shapes with simple primitives in high accuracy is important for a variety of applications in computer graphics and geometry processing. Existing solutions may produce suboptimal samples or are complex to implement. We present methods to approximate given shapes with user-tunable number of spheres to balance between accuracy and simplicity: touching medial/scale-axis polar balls and k-means smallest enclosing circles. Our methods are easy to implement, run efficiently, and can approach quality similar to manual construction. | Simple Methods to Represent Shapes with Sample Spheres | 10,394 |
Synthesizing realistic images involves computing high-dimensional light-transport integrals. In practice, these integrals are numerically estimated via Monte Carlo integration. The error of this estimation manifests itself as conspicuous aliasing or noise. To ameliorate such artifacts and improve image fidelity, we propose a perception-oriented framework to optimize the error of Monte Carlo rendering. We leverage models based on human perception from the halftoning literature. The result is an optimization problem whose solution distributes the error as visually pleasing blue noise in image space. To find solutions, we present a set of algorithms that provide varying trade-offs between quality and speed, showing substantial improvements over prior state of the art. We perform evaluations using quantitative and error metrics, and provide extensive supplemental material to demonstrate the perceptual improvements achieved by our methods. | Perceptual error optimization for Monte Carlo rendering | 10,395 |
Modern rendering libraries provide unprecedented realism, producing real-time photorealistic 3D graphics on commodity hardware. Visual fidelity, however, comes at the cost of increased complexity and difficulty of usage, with many rendering parameters requiring a deep understanding of the pipeline. We propose EasyPBR as an alternative rendering library that strikes a balance between ease-of-use and visual quality. EasyPBR consists of a deferred renderer that implements recent state-of-the-art approaches in physically based rendering. It offers an easy-to-use Python and C++ interface that allows high-quality images to be created in only a few lines of code or directly through a graphical user interface. The user can choose between fully controlling the rendering pipeline or letting EasyPBR automatically infer the best parameters based on the current scene composition. The EasyPBR library can help the community to more easily leverage the power of current GPUs to create realistic images. These can then be used as synthetic data for deep learning or for creating animations for academic purposes. | EasyPBR: A Lightweight Physically-Based Renderer | 10,396 |
We extend the incremental potential contact (IPC) model for contacting elastodynamics to resolve systems composed of codimensional DOFs in arbitrary combination. This enables a unified, interpenetration-free, robust, and stable simulation framework that couples codimension-0,1,2, and 3 geometries seamlessly with frictional contact. Extending IPC to thin structures poses new challenges in computing strain, modeling thickness and determining collisions. To address these challenges we propose three corresponding contributions. First, we introduce a C2 constitutive barrier model that directly enforces strain limiting as an energy potential while preserving rest state. This provides energetically-consistent strain limiting models (both isotropic and anisotropic) for cloth that enable strict satisfaction of strain-limit inequalities with direct coupling to both elastodynamics and contact via minimization of the incremental potential. Second, to capture the geometric thickness of codimensional domains we extend the IPC model to directly enforce distance offsets. Our treatment imposes a strict guarantee that mid-surfaces (resp. mid-lines) of shells (resp. rods) will not move closer than applied thickness values. This enables us to account for thickness in the contact behavior of codimensional structures and so robustly capture challenging contacting geometries; a number of which, to our knowledge, have not been simulated before. Third, codimensional models, especially with modeled thickness, mandate strict accuracy requirements that pose a severe challenge to all existing continuous collision detection (CCD) methods. To address these limitations we develop a new, efficient, simple-to-implement additive CCD (ACCD) method that applies conservative advancement to iteratively refine a lower bound for deforming primitives, converging to time of impact. | Codimensional Incremental Potential Contact | 10,397 |
We present a novel representation of compressed data structure for simultaneous bounding volume hierarchy (BVH) traversals like they appear for instance in collision detection & proximity query. The main idea is to compress bounding volume (BV) descriptors and cluster BVH into a smaller parts 'treelet' that fit into CPU cache while at the same time maintain random-access and automatic cache-aware data structure layouts. To do that, we quantify BV and compress 'treelet' using predictor-corrector scheme with the predictor at a specific node in the BVH based on the chain of BVs upwards. | Compressed Bounding Volume Hierarchies for Collision Detection &
Proximity Query | 10,398 |
Repetitive patterns are ubiquitous in natural and human-made objects, and can be created with a variety of tools and methods. Manual authoring provides unmatched degree of freedom and control, but can require significant artistic expertise and manual labor. Computational methods can automate parts of the manual creation process, but are mainly tailored for discrete pixels or elements instead of more general continuous structures. We propose an example-based method to synthesize continuous curve patterns from exemplars. Our main idea is to extend prior sample-based discrete element synthesis methods to consider not only sample positions (geometry) but also their connections (topology). Since continuous structures can exhibit higher complexity than discrete elements, we also propose robust, hierarchical synthesis to enhance output quality. Our algorithm can generate a variety of continuous curve patterns fully automatically. For further quality improvement and customization, we also present an autocomplete user interface to facilitate interactive creation and iterative editing. We evaluate our methods and interface via different patterns, ablation studies, and comparisons with alternative methods. | Continuous Curve Textures | 10,399 |
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