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"doi": "10.1109/CVPR52688.2022.00552",
"title": "SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Video",
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"abstract": "We propose SelfRecon, a clothed human body reconstruction method that combines implicit and explicit repre-sentations to recover space-time coherent geometries from a monocular self-rotating human video. Explicit methods require a predefined template mesh for a given sequence, while the template is hard to acquire for a specific subject. Meanwhile, the fixed topology limits the reconstruction accuracy and clothing types. Implicit representation supports arbitrary topology and can represent high-fidelity geometry shapes due to its continuous nature. However, it is difficult to integrate multi-frame information to produce a consistent registration sequence for downstream applications. We propose to combine the advantages of both representations. We utilize differential mask loss of the explicit mesh to obtain the coherent overall shape, while the details on the implicit surface are refined with the differentiable neural rendering. Meanwhile, the explicit mesh is updated periodically to adjust its topology changes, and a consistency loss is designed to match both representations. Compared with existing methods, SelfRecon can produce high-fidelity surfaces for arbitrary clothed humans with self-supervised optimization. Extensive experimental results demonstrate its effectiveness on real captured monocular videos. The source code is available at https://github.com/jby1993/SelfReconCode.",
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"affiliation": "University of Science and Technology of China",
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"abstract": "In recent years we have witnessed the rapid improvement of algorithms and technologies in object detection, instance segmentation and 3d reconstruction. Since the development of the R-CNN model and various improvements that follows, it is now an easy task to separate objects from the environment. For 3d reconstruction, Mesh R-CNN and PiFUHD render objects close to their original geometry, and this leads us to develop a 3d object reconstruction system that can integrate and improve its performance based on two models. We find that Mesh R-CNN can be improved with the newest PointRend model that generates more accurate shapes than Mask R-CNN on which Mesh R-CNN is based, and we reach the conclusion that a Monocular Instance Level 3D Object Reconstruction is fully feasible. document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.",
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"title": "Capturing Implicit Spatial Cues for Monocular 3d Hand Reconstruction",
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"abstract": "With the development of the parameterized hand model (e.g. MANO), it is possible to reconstruct the 3D hand mesh from a single 2D hand image by learning a few hand model parameters, rather than estimating hundreds of vertices on the mesh. However, it is highly non-linear to learn these parameters from the 2D hand image, as there is no explicit spatial correspondence between these parameters and image pixels. In this paper, we successfully leverage the graph convolutional network (GCN) to capture implicit spatial cues for fitting the well-known MANO hand model, thus greatly improving the performance of monocular 3D hand reconstruction. Our proposed MANO-GCN establishes the spatial hand mesh and hand joints graph for learning MANO parameters, with node features propagated along edges to utilize the spatial information. Among all monocular 3D hand reconstruction methods with MANO hand model, MANO-GCN achieves state-of-the-art accuracy on public FreiHAND and HO-3D benchmarks, without any bells and whistles. Code is available at https://github.com/ChenJoya/manogcn.",
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"content": "With the development of the parameterized hand model (e.g. MANO), it is possible to reconstruct the 3D hand mesh from a single 2D hand image by learning a few hand model parameters, rather than estimating hundreds of vertices on the mesh. However, it is highly non-linear to learn these parameters from the 2D hand image, as there is no explicit spatial correspondence between these parameters and image pixels. In this paper, we successfully leverage the graph convolutional network (GCN) to capture implicit spatial cues for fitting the well-known MANO hand model, thus greatly improving the performance of monocular 3D hand reconstruction. Our proposed MANO-GCN establishes the spatial hand mesh and hand joints graph for learning MANO parameters, with node features propagated along edges to utilize the spatial information. Among all monocular 3D hand reconstruction methods with MANO hand model, MANO-GCN achieves state-of-the-art accuracy on public FreiHAND and HO-3D benchmarks, without any bells and whistles. Code is available at https://github.com/ChenJoya/manogcn.",
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"abstract": "Video summarization provides a condensed version of a video stream by analyzing the video content. Automatic summarization of consumer videos is an important tool that facilitates efficient browsing, searching, and album creation in large consumer video collections. This paper studies automatic video summarization in the consumer domain where most previous methods cannot be easily applied due to the challenging issues for content analysis, i.e., consumer videos are captured with uncontrolled conditions such as uneven illumination, clutter, and large camera motion, and with poor-quality soundtrack as a mix of multiple sound sources under severe noise. To pursue reliable summarization, a case study with actual consumer users is conducted, from which a set of consumer-oriented guidelines is obtained. The guidelines reflect the high-level semantic rules, in both visual and audio aspects, which are recognized by consumers as important to produce good video summaries. Following these guidelines, an automatic video summarization algorithm is developed where both visual and audio information are used to generate improved summaries. To the best of our knowledge, this is a first systematic study on automatic summarization of consumer-quality videos. Experimental evaluations from consumer subjects show the effectiveness of our approach.",
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"abstract": "In this paper we propose an adaptive Augmented Reality interface for hand gestures based on a probabilistic model. The proposed method provides an in-situ interface and the corresponding functionalities by recognizing a context of hand shape and gesture which requires the accurate recognition of static and dynamic hand states. We present an appearance-based hand feature representation that yields robustness against hand shape variations, and a feature extraction method based on the fingertip likelihood from a GMM model. Experimental results show that both context-sensitivity and accurate hand gesture recognition are achieved throughout the quantitative evaluation and its implementation as a three-in-one virtual interface.",
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"abstract": "Based on the virtual reality (VR) and augmented reality (AR) technologies, the virtual reality interactive model platform for online teaching of engineering graphics is developed using Unity3D development platform, 3ds MAX, UV modeling software and C# interactive programming tools. The platform's underlying design, functional and technical development scheme, content design, and management strategy of the platform are introduced in detail. The platform can give full play to the respective advantages of VR and AR technologies, which can help overcome the obstacles in model display and interaction in online teaching of engineering drawing courses and provide a useful trial and exploration for the information construction of engineering drawing courses.",
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"affiliation": "Koç University, University College London",
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"abstract": "augmented reality applications overlap virtual objects over a real scene considering the context. Today, more advanced applications also make use of diminished reality, which removes real objects from a scene. This paper describes a novel approach that combines augmented reality and diminished reality techniques to modify real objects in augmented reality applications. The proposed approach removes an object and replaces it with its purposely-modified replica. The solution uses dynamic texture techniques and inpaint to enhance the visual response of the performed modification. The results are promising considering both realism of the modified real object and performance of the application.",
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"abstract": "Far infrared convection combined heat transfer is one of the best methods for energy saving and drying. In this paper, a comprehensive experimental study and analysis of the far infrared convection combined drying was carried out, to explore the intrinsic link and interaction between parameters in the drying process. Far infrared convection combined heat transfer was discussed in detail. The test was performed on the self-made test bench. The two orthogonal rotation regression design was made to get the regression equation, and by analyzing the corresponding heat transfer response surface, the relationship between the parameters on heat transfer efficiency was obtained.",
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"content": "Far infrared convection combined heat transfer is one of the best methods for energy saving and drying. In this paper, a comprehensive experimental study and analysis of the far infrared convection combined drying was carried out, to explore the intrinsic link and interaction between parameters in the drying process. Far infrared convection combined heat transfer was discussed in detail. The test was performed on the self-made test bench. The two orthogonal rotation regression design was made to get the regression equation, and by analyzing the corresponding heat transfer response surface, the relationship between the parameters on heat transfer efficiency was obtained.",
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"abstract": "Augmented reality (AR) neuronavigation has been proposed to address the shortcomings of conventional neuron-avigators. Researchers have presented low-cost AR methods for craniotomy planning, but they lack navigation capabilities. Other studies introduce AR neuronavigation systems that are a step further in usability than traditional neuronavigators, but they may be hard to obtain or reproduce. AR neuronavigation was also implemented on mobile devices, but most systems have an undesired lag during the navigation. This work investigates the feasibility of creating an accurate and low-cost standalone mobile AR neuronavigator. Unlike other mobile approaches, this solution has no perceptible lag as the processing is efficiently performed on the device instead of an external computer. Results show that a neuronavigation system can be deployed on a mobile device, running smoothly at 60 frames per second, and achieving a smaller than 5 mm target registration error.",
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"content": "We explore the benefits of using online-autotuning to find an optimal configuration for the parallel construction of Surface Area Heuristic (SAH) kD-trees. Using a quickly converging autotuning mechanism, we achieve a significant performance improvement of up to 1.96x. The SAH kD-tree is a spatial data structure and a fundamental tool in the domain of computer graphics and simulations. The parallel construction of these trees is influenced by several parameters, controlling various aspects of the algorithm. However, the parameter configurations advocated in the literature are hardly ever portable. To boost portability, we apply onlineautotuning to four state-of-the-art variants of parallel kD-tree construction. We show that speedups over the variants' standard configurations are possible with low programmer effort. We further demonstrate the performance portability of our approach by evaluating performance on varying multicore platforms and both static and dynamic geometries.",
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"title": "Enhancing Autotuning Capability with a History Database",
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"abstract": "Autotuning is gaining importance to achieve the best possible performance for exascale applications. The performance of an autotuner usually depends on the amount of performance data collected for the application, however, collecting performance data for large-scale applications is oftentimes an expensive and daunting task. This paper presents an autotuner database, which we call a history database, for enhancing the reusability and reproducibility of performance data. The history database is built into a publicly available autotuner called GPTune, and allows users to store performance data obtained from autotuning and download historical performance data provided by the same or other users. The database not only allows reuse of the best available tuning results for widely used codes but also enables transfer learning that can leverage knowledge of pre-trained performance models. An evaluation shows that, for ScaLAPACK's PDGEQRF routine, a transfer learning approach using the history database can attain up to 33% better tuning results compared to single task learning without using prior knowledge, on 2,048 cores of NERSC's Cori supercomputer.",
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"content": "Autotuning is gaining importance to achieve the best possible performance for exascale applications. The performance of an autotuner usually depends on the amount of performance data collected for the application, however, collecting performance data for large-scale applications is oftentimes an expensive and daunting task. This paper presents an autotuner database, which we call a history database, for enhancing the reusability and reproducibility of performance data. The history database is built into a publicly available autotuner called GPTune, and allows users to store performance data obtained from autotuning and download historical performance data provided by the same or other users. The database not only allows reuse of the best available tuning results for widely used codes but also enables transfer learning that can leverage knowledge of pre-trained performance models. An evaluation shows that, for ScaLAPACK's PDGEQRF routine, a transfer learning approach using the history database can attain up to 33% better tuning results compared to single task learning without using prior knowledge, on 2,048 cores of NERSC's Cori supercomputer.",
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"affiliation": "University of California, Berkeley,Department of Electrical Engineering and Computer Sciences,Berkeley,CA,USA",
"fullName": "James W. Demmel",
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"affiliation": "Lawrence Berkeley National Laboratory,Computational Research Division,Berkeley,CA,USA",
"fullName": "Xiaoye S. Li",
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"affiliation": "Lawrence Berkeley National Laboratory,Computational Research Division,Berkeley,CA,USA",
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"abstract": "We propose a photo-realistic method for artificially ageing facial photographs by combining learned shape deformations with skin detail transfer between a donor and a receiver face. Facial ageing is a complicated process that most existing face models, such as 3d Morphable Models fail to express, due to lacking correspondence between the wrinkles of different individuals. We propose an exemplar-based approach to face ageing, where we transfer high-frequency details from an older face texture to a younger receiver. By warping the resulting image according to a learned shape ageing deformation, we obtain photo-realistic aged photographs. We evaluate the simulator with human perception experiments showing that we can indeed create results that are perceived to be real.",
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"title": "VR Sickness Assessment with Perception Prior and Hybrid Temporal Features",
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"abstract": "Virtual reality (VR) sickness is one of the obstacles hindering the growth of the VR market. Different VR contents may cause various degree of sickness. If the degree of the sickness can be estimated objectively, it adds a great value and help in designing the VR contents. To address this problem, a novel content-based VR sickness assessment method which considers both the perception prior and hybrid temporal features is proposed. Based on the perception prior which assumes the user's field of view becomes narrower while watching videos, a Gaussian weighted optical flow is calculated with a specified aspect ratio. In order to capture the dynamic characteristics, hybrid temporal features including horizontal motion, vertical motion and the proposed motion anisotropy are adopted. In addition, a new dataset is compiled with one hundred VR sickness test samples and each of which comes along with the Discomfort Scores (DS) answered by the user and a Simulator Sickness Questionnaire (SSQ) collected at the end of test. A random forest regressor is then trained on this dataset by feeding the hybrid temporal features of both the present and the previous minute. Extensive experiments are conducted on the VRSA dataset and the results demonstrate that the proposed method is comparable to the state-of-the-art method in terms of effectiveness and efficiency.",
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"abstracts": [
{
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"content": "We propose a method to precisely segment books on bookshelves in images taken from general viewpoints. The proposed segmentation algorithm overcomes difficulties due to text and texture on book spines, various book orientations under perspective projection, and book proximity. A shape dependent active contour is used as a first step to establish a set of book spine candidates. A subset of these candidates are selected using spatial constraints on the assembly of spine candidates by formulating the selection problem as the maximal weighted independent set (MWIS) of a graph. The segmented book spines may be used by recognition systems (e.g., library automation), or rendered in computer graphics applications. We also propose a novel application that uses the segmented book spines to assist users in bookshelf reorganization or to modify the image to create a bookshelf with a tidier look. Our method was successfully tested on challenging sets of images.",
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"affiliation": "The Interdisciplinary Center, Herzliya 46150, Israel",
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"affiliation": "The Interdisciplinary Center, Herzliya 46150, Israel",
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"proceeding": {
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"title": "2015 Seventh International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)",
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"article": {
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"doi": "10.1109/ICMTMA.2015.96",
"title": "Detection and Evaluation on the Spine Tracking Accuracy during Image-Guided Radiation Therapy",
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"abstract": "Utilize the head and neck phantom, lung phantom and simulated human phantom to detect the spine tracking accuracy of Cyber Knife during IGRT, and give evaluation. Use CT to scan the head and neck phantom, lung phantom and simulated human phantom which have films set in, then make use of the treatment planning system to design plans for tracking cervical vertebra, thoracic vertebra, lumbar vertebra, caudal vertebra and thigh bone. Cyber Knife carries out the plans for phantoms, and E2E software is used to analyze the irradiating accuracy. Results The accuracy of tracking cervical vertebra was 0.54 mm and 0.68 mm, thoracic vertebra, 0.50 mm and 0.65 mm, lumbar vertebra, 0.70 mm, sacral vertebrae, 1.99 mm, ilium, 2.21 mm, thigh bone, 2.05 mm. For these 3 static phantoms, the accuracy of tracking cervical vertebra, thoracic vertebra and lumbar vertebra all reached the sub millimeter level, and the tracking accuracy of sacral vertebrae, ilium and thigh bone was 2±0.21 mm. So the spine tracking accuracy is quite good.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Utilize the head and neck phantom, lung phantom and simulated human phantom to detect the spine tracking accuracy of Cyber Knife during IGRT, and give evaluation. Use CT to scan the head and neck phantom, lung phantom and simulated human phantom which have films set in, then make use of the treatment planning system to design plans for tracking cervical vertebra, thoracic vertebra, lumbar vertebra, caudal vertebra and thigh bone. Cyber Knife carries out the plans for phantoms, and E2E software is used to analyze the irradiating accuracy. Results The accuracy of tracking cervical vertebra was 0.54 mm and 0.68 mm, thoracic vertebra, 0.50 mm and 0.65 mm, lumbar vertebra, 0.70 mm, sacral vertebrae, 1.99 mm, ilium, 2.21 mm, thigh bone, 2.05 mm. For these 3 static phantoms, the accuracy of tracking cervical vertebra, thoracic vertebra and lumbar vertebra all reached the sub millimeter level, and the tracking accuracy of sacral vertebrae, ilium and thigh bone was 2±0.21 mm. So the spine tracking accuracy is quite good.",
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],
"normalizedAbstract": "Utilize the head and neck phantom, lung phantom and simulated human phantom to detect the spine tracking accuracy of Cyber Knife during IGRT, and give evaluation. Use CT to scan the head and neck phantom, lung phantom and simulated human phantom which have films set in, then make use of the treatment planning system to design plans for tracking cervical vertebra, thoracic vertebra, lumbar vertebra, caudal vertebra and thigh bone. Cyber Knife carries out the plans for phantoms, and E2E software is used to analyze the irradiating accuracy. Results The accuracy of tracking cervical vertebra was 0.54 mm and 0.68 mm, thoracic vertebra, 0.50 mm and 0.65 mm, lumbar vertebra, 0.70 mm, sacral vertebrae, 1.99 mm, ilium, 2.21 mm, thigh bone, 2.05 mm. For these 3 static phantoms, the accuracy of tracking cervical vertebra, thoracic vertebra and lumbar vertebra all reached the sub millimeter level, and the tracking accuracy of sacral vertebrae, ilium and thigh bone was 2±0.21 mm. So the spine tracking accuracy is quite good.",
"fno": "7143a374",
"keywords": [
"Phantoms",
"Accuracy",
"Bones",
"Tumors",
"Head",
"Lungs",
"Neck",
"Accuracy",
"Cyber Knife",
"IGRT",
"Spine Tracking"
],
"authors": [
{
"affiliation": null,
"fullName": "Li Yu",
"givenName": "Li",
"surname": "Yu",
"__typename": "ArticleAuthorType"
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{
"affiliation": null,
"fullName": "Xu Hui-Jun",
"givenName": "Xu",
"surname": "Hui-Jun",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Zhang Su-Jing",
"givenName": "Zhang",
"surname": "Su-Jing",
"__typename": "ArticleAuthorType"
},
{
"affiliation": null,
"fullName": "Yang Xiao",
"givenName": "Yang",
"surname": "Xiao",
"__typename": "ArticleAuthorType"
}
],
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"pubDate": "2015-06-01T00:00:00",
"pubType": "proceedings",
"pages": "374-376",
"year": "2015",
"issn": null,
"isbn": "978-1-4673-7143-8",
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