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"abstract": "Synthesizing speech-driven facial animation is the process of animating a virtual face according to the input audio signal. Actually, audio-to-visual conversion is the core of speech-driven facial animation. In this paper, Gaussian Mixture Models (GMM) are employed for audio-to-visual conversion. The conventional GMM based method performs the conversion frame by frame using minimum mean square error estimation. We consider two issues related to the conventional method: 1) the influence of previous visual features on current visual feature is not considered, and 2) GMM training and conversion are inconsistent. To address these issues, we propose incorporating previous visual features into the conversion. We also propose a minimum conversion error based approach to refine the GMM parameters. Experiments on a public available database show that our method can accurately convert audio features into visual features. The conversion accuracy is comparable to a current state-of-the-art trajectory-based approach. Based on the proposed method, we develop a speech-driven facial animation system, the system runs in real time and outputs realistic speech animations.",
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"abstract": "This paper present an application to the facial animation of the D.O.G.M.A. (Deformation Of Geometrical Model Animated) model. We described an order-controlled animation, where a first set of orders simulate the contraction of different facial muscles, a second some facial expressions. The main interest of this method is the manipulation of faces with various shape. Since the deformation given by the animation model DOGMA are space deformations, the only necessity is a hierarchical structuring of the different face component. With this method the same facial animation order can easily be used for various objects with different shapes, if they present the same hierarchical structuring.",
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"content": "This paper present an application to the facial animation of the D.O.G.M.A. (Deformation Of Geometrical Model Animated) model. We described an order-controlled animation, where a first set of orders simulate the contraction of different facial muscles, a second some facial expressions. The main interest of this method is the manipulation of faces with various shape. Since the deformation given by the animation model DOGMA are space deformations, the only necessity is a hierarchical structuring of the different face component. With this method the same facial animation order can easily be used for various objects with different shapes, if they present the same hierarchical structuring.",
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"abstract": "In this paper, a system for speech-driven animation of generic 3D head models is presented. The system is based on the inversion of a joint Audio-Visual Hidden Markov Model to estimate the visual information from speech data. Estimated visual speech features are used to animate a simple face model. The animation of a more complex head model is then obtained by automatically mapping the deformation of the simple model to it. The proposed algorithm allows the animation of 3D head models of arbitrary complexity through a simple setup procedure. The resulting animation is evaluated in terms of intelligibility of visual speech through subjective tests, showing a promising performance.",
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"affiliation": "Max Planck Institute for Intelligent Systems,Tübingen,Germany",
"fullName": "Sai Kumar Dwivedi",
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"affiliation": "Max Planck Institute for Intelligent Systems,Tübingen,Germany",
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"affiliation": "Max Planck Institute for Intelligent Systems,Tübingen,Germany",
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"abstract": "In order to solve the problem that the existing 2D video communication has a high bandwidth occupancy rate, the inability to present three-dimensional portraits of characters and the high dependence of three-dimensional projection technology on hardware, we propose a 3D communication framework that integrates 3D reconstruction and rendering display. The framework transmits the video stream to the video encoding module. The video coding module mainly completes the work of face detection, key point feature extraction and depth map feature coding. It is transmitted to the terminal device through the transmission protocol for video decoding and reconstruction. Then combined with the 3D rendering algorithm, virtual viewpoint synthesis and stereo image encoding are performed to realize the 3D reconstruction of the real face. The frame structure opens up the technical path from ordinary 2D video acquisition to 3D communication system implementation. Experiments show that the use of this communication framework for real face 3D reconstruction has higher accuracy and better information fusion. The reconstruction rate can reach 15fps.",
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"content": "In order to solve the problem that the existing 2D video communication has a high bandwidth occupancy rate, the inability to present three-dimensional portraits of characters and the high dependence of three-dimensional projection technology on hardware, we propose a 3D communication framework that integrates 3D reconstruction and rendering display. The framework transmits the video stream to the video encoding module. The video coding module mainly completes the work of face detection, key point feature extraction and depth map feature coding. It is transmitted to the terminal device through the transmission protocol for video decoding and reconstruction. Then combined with the 3D rendering algorithm, virtual viewpoint synthesis and stereo image encoding are performed to realize the 3D reconstruction of the real face. The frame structure opens up the technical path from ordinary 2D video acquisition to 3D communication system implementation. Experiments show that the use of this communication framework for real face 3D reconstruction has higher accuracy and better information fusion. The reconstruction rate can reach 15fps.",
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"title": "Soft Rasterizer: A Differentiable Renderer for Image-Based 3D Reasoning",
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"abstract": "Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation. By inverting such renderer, one can think of a learning approach to infer 3D information from 2D images. However, standard graphics renderers involve a fundamental discretization step called rasterization, which prevents the rendering process to be differentiable, hence able to be learned. Unlike the state-of-the-art differentiable renderers, which only approximate the rendering gradient in the back propagation, we propose a truly differentiable rendering framework that is able to (1) directly render colorized mesh using differentiable functions and (2) back-propagate efficient supervision signals to mesh vertices and their attributes from various forms of image representations, including silhouette, shading and color images. The key to our framework is a novel formulation that views rendering as an aggregation function that fuses the probabilistic contributions of all mesh triangles with respect to the rendered pixels. Such formulation enables our framework to flow gradients to the occluded and far-range vertices, which cannot be achieved by the previous state-of-the-arts. We show that by using the proposed renderer, one can achieve significant improvement in 3D unsupervised single-view reconstruction both qualitatively and quantitatively. Experiments also demonstrate that our approach is able to handle the challenging tasks in image-based shape fitting, which remain nontrivial to existing differentiable renderers. Code is available at https://github.com/ShichenLiu/SoftRas.",
"abstracts": [
{
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"content": "Rendering bridges the gap between 2D vision and 3D scenes by simulating the physical process of image formation. By inverting such renderer, one can think of a learning approach to infer 3D information from 2D images. However, standard graphics renderers involve a fundamental discretization step called rasterization, which prevents the rendering process to be differentiable, hence able to be learned. Unlike the state-of-the-art differentiable renderers, which only approximate the rendering gradient in the back propagation, we propose a truly differentiable rendering framework that is able to (1) directly render colorized mesh using differentiable functions and (2) back-propagate efficient supervision signals to mesh vertices and their attributes from various forms of image representations, including silhouette, shading and color images. The key to our framework is a novel formulation that views rendering as an aggregation function that fuses the probabilistic contributions of all mesh triangles with respect to the rendered pixels. Such formulation enables our framework to flow gradients to the occluded and far-range vertices, which cannot be achieved by the previous state-of-the-arts. We show that by using the proposed renderer, one can achieve significant improvement in 3D unsupervised single-view reconstruction both qualitatively and quantitatively. Experiments also demonstrate that our approach is able to handle the challenging tasks in image-based shape fitting, which remain nontrivial to existing differentiable renderers. Code is available at https://github.com/ShichenLiu/SoftRas.",
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"keywords": [
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"authors": [
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"affiliation": "University of Southern California",
"fullName": "Shichen Liu",
"givenName": "Shichen",
"surname": "Liu",
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"affiliation": "USC Institute for Creative Technology",
"fullName": "Weikai Chen",
"givenName": "Weikai",
"surname": "Chen",
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{
"affiliation": "University of Southern California",
"fullName": "Tianye Li",
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"surname": "Li",
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"content": "The main challenge of sketch-based 3D shape retrieval is the large cross-modal differences between 2D sketches and 3D shapes. Most recent works employed two heterogeneous networks and a shared loss to directly map the features from different modalities to a common feature space, which failed to reduce the cross-modal differences effectively. In this paper, we propose a novel method that adopts a teacher-student strategy to learn an aligned cross-modal feature space indirectly. Specifically, our method first employs a classification network to learn the discriminative features of 3D shapes. Then, the pre-learned features are considered as a teacher to guide the feature learning of 2D sketches. In order to align the cross-modal features, 2D sketch features are transferred to the pre-learned 3D feature space. Our experiments on two benchmark datasets demonstrate that our method obtains superior retrieval performance than the state-of-the-art approaches.",
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"abstract": "Hair is one of the most critical characteristics of a person in the process of digitizing characters, but at the same time, hair modeling is still a very challenging task due to the diversity of hairstyles and the overlap among hair. We introduce a method to automatically generate 3D hair geometry from a front hair image, which can recover the outline and details of the hair geometry. We designed a encoder-decoder convolutional neural network which takes the 2D orientation field from a hair image as input, and output the characteristic hair geometry. Then we use the characteristic strands to search for eligible hairstyle from Hairstyle-database, and fuse retrieved hair model to get the nal hairstyle. This pipeline can automatically recover hair geometry from a front hair image without any supplementary information. Experimental results show that our approach achieves realistic reconstruction effect from real Internet pictures and self-portraits.",
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"abstract": "The measurement of eyestrain caused by watching stereoscopic 3D content is very important in producing 3D movies and 3D TV content that may be comfortably watched by the viewer. Some research has been done regarding comparative measurements of eyestrain between 2D and 3D video. However, what has not been explored sufficiently is the eyestrain caused by variations in binocular disparity, viewing time and display size in watching stereoscopic 3D video. In this paper, we quantified viewer discomfort by measuring eye blinking rates using an eye tracker system, and then estimated the user's eyestrain by integrating the eye blinking rates with user's subjective test responses when watching stereoscopic 3D content in relation to binocular disparity, viewing time and display size. As might have been expected, the experimental results show that eyestrain increases as the binocular disparity and viewing time increase. We also find that the viewer's eyestrain caused from watching stereoscopic 3D content on small displays is higher than with larger displays.",
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"abstract": "Redirected walking techniques have the potential to provide natural locomotion while users experience large virtual environments. However, when using redirected walking in small physical workspaces, disruptive overt resets are often required. We describe the design of an educational virtual reality experience in which users physically walk through virtual tunnels representative of the World War I battle of Vauquois. Walking in only a 15- by 5-foot tracked space, users are redirected through subtle, narrative-driven resets to walk through a tunnel nearly 50 feet in length. This work contributes approaches and lessons that can be used to provide a seamless and natural virtual reality walking experience in highly constrained physical spaces.",
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"title": "I Can See on My Feet While Walking: Sensitivity to Translation Gains with Visible Feet",
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"abstract": "Redirected walking allows users to explore immersive virtual environments by real walking even when the physical tracking space is limited. Redirected walking is usually implemented via translation gains, rotation gains, and curvature gains, while previous research was focused on identifying detection thresholds for such manipulations. To our knowledge, all previous experiments were conducted without a visual self-representation of the user in the virtual environment, in particular, without showing the user's feet. In this paper, we address the question if the virtual self-representation of the user's feet changes the detection thresholds for translation gains. Furthermore, we consider the influence of the holisticness of the visual stimulus, i. e., the type of virtual environment. Therefore, we conducted an experiment to identify detection thresholds for translation gains under three different conditions: (i) without visible virtual feet and (ii) with visible virtual feet both in a high fidelity visually rich virtual environment, and (iii) with visible virtual feet in a low cue virtual environment. The results revealed the range of detection thresholds for translations gains, which cannot be detected by the user when the feet are visible. Furthermore, the results show a significant difference between the two types of environment. Our findings suggest that the virtual environment is more important for manipulation detection than the visual self-representation of the user's feet.",
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"content": "Redirected walking allows users to explore immersive virtual environments by real walking even when the physical tracking space is limited. Redirected walking is usually implemented via translation gains, rotation gains, and curvature gains, while previous research was focused on identifying detection thresholds for such manipulations. To our knowledge, all previous experiments were conducted without a visual self-representation of the user in the virtual environment, in particular, without showing the user's feet. In this paper, we address the question if the virtual self-representation of the user's feet changes the detection thresholds for translation gains. Furthermore, we consider the influence of the holisticness of the visual stimulus, i. e., the type of virtual environment. Therefore, we conducted an experiment to identify detection thresholds for translation gains under three different conditions: (i) without visible virtual feet and (ii) with visible virtual feet both in a high fidelity visually rich virtual environment, and (iii) with visible virtual feet in a low cue virtual environment. The results revealed the range of detection thresholds for translations gains, which cannot be detected by the user when the feet are visible. Furthermore, the results show a significant difference between the two types of environment. Our findings suggest that the virtual environment is more important for manipulation detection than the visual self-representation of the user's feet.",
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"normalizedAbstract": "Redirected walking allows users to explore immersive virtual environments by real walking even when the physical tracking space is limited. Redirected walking is usually implemented via translation gains, rotation gains, and curvature gains, while previous research was focused on identifying detection thresholds for such manipulations. To our knowledge, all previous experiments were conducted without a visual self-representation of the user in the virtual environment, in particular, without showing the user's feet. In this paper, we address the question if the virtual self-representation of the user's feet changes the detection thresholds for translation gains. Furthermore, we consider the influence of the holisticness of the visual stimulus, i. e., the type of virtual environment. Therefore, we conducted an experiment to identify detection thresholds for translation gains under three different conditions: (i) without visible virtual feet and (ii) with visible virtual feet both in a high fidelity visually rich virtual environment, and (iii) with visible virtual feet in a low cue virtual environment. The results revealed the range of detection thresholds for translations gains, which cannot be detected by the user when the feet are visible. Furthermore, the results show a significant difference between the two types of environment. Our findings suggest that the virtual environment is more important for manipulation detection than the visual self-representation of the user's feet.",
"fno": "08446216",
"keywords": [
"Human Computer Interaction",
"Virtual Reality",
"Redirected Walking",
"Immersive Virtual Environments",
"Rotation Gains",
"Curvature Gains",
"Visible Virtual Feet",
"High Fidelity Visually Rich Virtual Environment",
"Low Cue Virtual Environment",
"Feet Virtual Self Representation",
"Feet Visual Self Representation",
"Translation Gains Sensitivity",
"Legged Locomotion",
"Visualization",
"Foot",
"Virtual Environments",
"Tracking",
"Cameras",
"Avatars",
"Locomotion",
"Redirected Walking",
"Translation Gains H 5 1 Information Interfaces And Presentation Multimedia Information Systems X 2015 Artificial",
"Augmented",
"And Virtual Realities",
"I 3 7 Computer Graphics Three Dimensional Graphics And Realism X 2015 Virtual Reality"
],
"authors": [
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"affiliation": "University of Hamburg, Human-Computer Interaction",
"fullName": "Lucie Kruse",
"givenName": "Lucie",
"surname": "Kruse",
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{
"affiliation": "University of Hamburg, Human-Computer Interaction",
"fullName": "Eike Langbehn",
"givenName": "Eike",
"surname": "Langbehn",
"__typename": "ArticleAuthorType"
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{
"affiliation": "Human-Comput. Interaction, Univ. of Hamburg, Hamburg, Germany",
"fullName": "Frank Steinicke",
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"surname": "Steinicke",
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"id": "17D45VtKirz",
"title": "2018 IEEE 4th VR Workshop on Sonic Interactions for Virtual Environments (SIVE)",
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"doi": "10.1109/SIVE.2018.8577177",
"title": "Influence of hearing your steps and environmental sounds in VR while walking",
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"abstract": "Presence, the feeling of `being there' in a virtual environment (VR), is seen as a basic requirement for VR environments.In a controlled laboratory experiment, the effect of auditory components of VR on presence was studied. Participants wore a head-mounted display (Oculus Rift DK2) and noise-cancelling headphones (Bose Quiet Comfort 25) while walking on a treadmill for 5 minutes per experimental condition. They were visually presented with a rural virtual reality scenery. The audio presentation was varied in a 2×2 within-subjects design with a \"Soundscape\" (birds, wind, church bells) and/or \"Footsteps\" (played back when the sensors detected a step), either being presented or not. After each condition, the subjects filled out the IPQ Presence Questionnaire.Data analysis was conducted on 40 participants who completed each of the four sound conditions in random order. The results of the statistical analysis show that (a) a single-item presence scale (percentage of `perfect' presence), (b) the IPQ presence scale G1, and (c) the IPQ realism scale all detected significant effects of playing back footstep sounds, and even larger effects due to providing a soundscape in VR. By contrast, IPQ spatial presence was not increased and IPQ involvement was only increased by providing the environmental soundscape.To sum up, the self-generated sounds had significant effects on the feeling of \"being there\" and on perceived realism, while the effect of the soundscape tended to be even more powerful.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Presence, the feeling of `being there' in a virtual environment (VR), is seen as a basic requirement for VR environments.In a controlled laboratory experiment, the effect of auditory components of VR on presence was studied. Participants wore a head-mounted display (Oculus Rift DK2) and noise-cancelling headphones (Bose Quiet Comfort 25) while walking on a treadmill for 5 minutes per experimental condition. They were visually presented with a rural virtual reality scenery. The audio presentation was varied in a 2×2 within-subjects design with a \"Soundscape\" (birds, wind, church bells) and/or \"Footsteps\" (played back when the sensors detected a step), either being presented or not. After each condition, the subjects filled out the IPQ Presence Questionnaire.Data analysis was conducted on 40 participants who completed each of the four sound conditions in random order. The results of the statistical analysis show that (a) a single-item presence scale (percentage of `perfect' presence), (b) the IPQ presence scale G1, and (c) the IPQ realism scale all detected significant effects of playing back footstep sounds, and even larger effects due to providing a soundscape in VR. By contrast, IPQ spatial presence was not increased and IPQ involvement was only increased by providing the environmental soundscape.To sum up, the self-generated sounds had significant effects on the feeling of \"being there\" and on perceived realism, while the effect of the soundscape tended to be even more powerful.",
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"normalizedAbstract": "Presence, the feeling of `being there' in a virtual environment (VR), is seen as a basic requirement for VR environments.In a controlled laboratory experiment, the effect of auditory components of VR on presence was studied. Participants wore a head-mounted display (Oculus Rift DK2) and noise-cancelling headphones (Bose Quiet Comfort 25) while walking on a treadmill for 5 minutes per experimental condition. They were visually presented with a rural virtual reality scenery. The audio presentation was varied in a 2×2 within-subjects design with a \"Soundscape\" (birds, wind, church bells) and/or \"Footsteps\" (played back when the sensors detected a step), either being presented or not. After each condition, the subjects filled out the IPQ Presence Questionnaire.Data analysis was conducted on 40 participants who completed each of the four sound conditions in random order. The results of the statistical analysis show that (a) a single-item presence scale (percentage of `perfect' presence), (b) the IPQ presence scale G1, and (c) the IPQ realism scale all detected significant effects of playing back footstep sounds, and even larger effects due to providing a soundscape in VR. By contrast, IPQ spatial presence was not increased and IPQ involvement was only increased by providing the environmental soundscape.To sum up, the self-generated sounds had significant effects on the feeling of \"being there\" and on perceived realism, while the effect of the soundscape tended to be even more powerful.",
"fno": "08577177",
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"Headphones",
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"IPQ Spatial Presence",
"Single Item Presence Scale",
"Audio Presentation",
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"Bose Quiet Comfort 25",
"Oculus Rift DK 2",
"Head Mounted Display",
"Auditory Components",
"VR Environments",
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"authors": [
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"affiliation": "Technische Universität Darmstadt",
"fullName": "Angelika C. Kern",
"givenName": "Angelika C.",
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{
"affiliation": "Technische Universität Darmstadt",
"fullName": "Wolfgang Ellermeier",
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"title": "2017 International Conference on Culture and Computing (Culture and Computing)",
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"doi": "10.1109/Culture.and.Computing.2017.25",
"title": "Walk through a Museum with Binocular Stereo Effect and Spherical Panorama Views",
"normalizedTitle": "Walk through a Museum with Binocular Stereo Effect and Spherical Panorama Views",
"abstract": "Photo based spherical panoramas were usually used to represent scenes in art museum, but traditional panorama only allows looking around the scene from a fixed point and it could not provide binocular stereo effect that is very necessary to build up a more immersive experience for art appreciation. Modeling based virtual tour scene could provide walking through ability and binocular stereo effect but it is almost impossible to model an art museum as just it likes. In this paper, the authors designed a special method for spherical panorama recording, based on which kind of virtual tour system was developed to provide walking through ability and binocular stereo effect to users with head mounted display. This system combines the advantages of photo realistic, walking through and binocular stereo effect.",
"abstracts": [
{
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"content": "Photo based spherical panoramas were usually used to represent scenes in art museum, but traditional panorama only allows looking around the scene from a fixed point and it could not provide binocular stereo effect that is very necessary to build up a more immersive experience for art appreciation. Modeling based virtual tour scene could provide walking through ability and binocular stereo effect but it is almost impossible to model an art museum as just it likes. In this paper, the authors designed a special method for spherical panorama recording, based on which kind of virtual tour system was developed to provide walking through ability and binocular stereo effect to users with head mounted display. This system combines the advantages of photo realistic, walking through and binocular stereo effect.",
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"fno": "08227335",
"keywords": [
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"Helmet Mounted Displays",
"Museums",
"Stereo Image Processing",
"Virtual Reality",
"Binocular Stereo Effect",
"Art Museum",
"Spherical Panorama Recording",
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"Art",
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"affiliation": null,
"fullName": "YanXiang Zhang",
"givenName": "YanXiang",
"surname": "Zhang",
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"affiliation": null,
"fullName": "ZiQiang Zhu",
"givenName": "ZiQiang",
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"affiliation": null,
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"abstract": "Deep neural networks are facing a potential security threat from adversarial examples, inputs that look normal but cause an incorrect classification by the deep neural network. For example, the proposed threat could result in hand-written digits on a scanned check being incorrectly classified but looking normal when humans see them. This research assesses the extent to which adversarial examples pose a security threat, when one considers the normal image acquisition process. This process is mimicked by simulating the transformations that normally occur in of acquiring the image in a real world application, such as using a scanner to acquire digits for a check amount or using a camera in an autonomous car. These small transformations negate the effect of the carefully crafted perturbations of adversarial examples, resulting in a correct classification by the deep neural network. Thus just acquiring the image decreases the potential impact of the proposed security threat. We also show that the already widely used process of averaging over multiple crops neutralizes most adversarial examples. Normal preprocessing, such as text binarization, almost completely neutralizes adversarial examples. This is the first paper to show that for text driven classification, adversarial examples are an academic curiosity, not a security threat.",
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"content": "Deep neural networks are facing a potential security threat from adversarial examples, inputs that look normal but cause an incorrect classification by the deep neural network. For example, the proposed threat could result in hand-written digits on a scanned check being incorrectly classified but looking normal when humans see them. This research assesses the extent to which adversarial examples pose a security threat, when one considers the normal image acquisition process. This process is mimicked by simulating the transformations that normally occur in of acquiring the image in a real world application, such as using a scanner to acquire digits for a check amount or using a camera in an autonomous car. These small transformations negate the effect of the carefully crafted perturbations of adversarial examples, resulting in a correct classification by the deep neural network. Thus just acquiring the image decreases the potential impact of the proposed security threat. We also show that the already widely used process of averaging over multiple crops neutralizes most adversarial examples. Normal preprocessing, such as text binarization, almost completely neutralizes adversarial examples. This is the first paper to show that for text driven classification, adversarial examples are an academic curiosity, not a security threat.",
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"abstract": "Deep neural networks (DNNs) have made tremendous progress in many different areas in recent years. How these networks function internally, however, is often not well understood. Advances in under-standing DNNs will benefit and accelerate the development of the field. We present TNNVis, a visualization system that supports un-derstanding of deep neural networks specifically designed to analyze text. TNNVis focuses on DNNs composed of fully connected and convolutional layers. It integrates visual encodings and interaction techniques chosen specifically for our tasks. The tool allows users to: (1) visually explore DNN models with arbitrary input using a combination of node-link diagrams and matrix representation; (2) quickly identify activation values, weights, and feature map patterns within a network; (3) flexibly focus on visual information of interest with threshold, inspection, insight query, and tooltip operations; (4) discover network activation and training patterns through animation; and (5) compare differences between internal activation patterns for different inputs to the DNN. These functions allow neural network researchers to examine their DNN models from new perspectives, producing insights on how these models function. Clustering and summarization techniques are employed to support large convolutional and fully connected layers. Based on several part of speech models with different structure and size, we present multiple use cases where visualization facilitates an understanding of the models.",
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"abstract": "Deep neural networks (DNNs) are vulnerable to maliciously generated adversarial examples. These examples are intentionally designed by making imperceptible perturbations and often mislead a DNN into making an incorrect prediction. This phenomenon means that there is significant risk in applying DNNs to safety-critical applications, such as driverless cars. To address this issue, we present a visual analytics approach to explain the primary cause of the wrong predictions introduced by adversarial examples. The key is to analyze the datapaths of the adversarial examples and compare them with those of the normal examples. A datapath is a group of critical neurons and their connections. To this end, we formulate the datapath extraction as a subset selection problem and approximately solve it based on back-propagation. A multi-level visualization consisting of a segmented DAG (layer level), an Euler diagram (feature map level), and a heat map (neuron level), has been designed to help experts investigate datapaths from the high-level layers to the detailed neuron activations. Two case studies are conducted that demonstrate the promise of our approach in support of explaining the working mechanism of adversarial examples.",
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"abstract": "With the rapid growth of online videos, video advertising, as a main source of income for video streaming websites, has attracted increasing attention. It is challenging to insert advertisements at suitable positions while preserving comfortable watching experience of users. Existing methods usually insert advertisements at the fixed positions and neglect the variations of scenes, which can extremely reduce the attractiveness of videos due to intrusion to important visual elements. In this paper, we propose a method to automatically generate and embed appealing textual advertisements for online videos. First we estimate the visual significance of the main elements in the video frames via human face localization and saliency detection. Next we design an efficient algorithm to recognize the scene changes with the visual significance map, through which the system can find stable areas in distinct scenes for advertising. At last, a series of aesthetic designing principles are adopted to generate attractive advertisements which are in harmony with the style of video scenes. User studies show that our system can achieve the best user experience compared with the state-of-the-art methods as well as comparable results with commercial advertisements designed by professional designers.",
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"content": "With the rapid growth of online videos, video advertising, as a main source of income for video streaming websites, has attracted increasing attention. It is challenging to insert advertisements at suitable positions while preserving comfortable watching experience of users. Existing methods usually insert advertisements at the fixed positions and neglect the variations of scenes, which can extremely reduce the attractiveness of videos due to intrusion to important visual elements. In this paper, we propose a method to automatically generate and embed appealing textual advertisements for online videos. First we estimate the visual significance of the main elements in the video frames via human face localization and saliency detection. Next we design an efficient algorithm to recognize the scene changes with the visual significance map, through which the system can find stable areas in distinct scenes for advertising. At last, a series of aesthetic designing principles are adopted to generate attractive advertisements which are in harmony with the style of video scenes. User studies show that our system can achieve the best user experience compared with the state-of-the-art methods as well as comparable results with commercial advertisements designed by professional designers.",
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"abstract": "Our goal is to develop tools that go beyond frame-constrained manipulation such as resizing, color correction, and simple transitions, and provide object-level operations within frames. Some of our targeted video editing tasks includes transferring a motion picture to a new still picture, importing a moving object into a new background, and compositing two video sequences. The challenges behind this kind of complex video editing tasks lie in two constraints: 1) Spatial consistency: imported objects should blend with the background seamlessly. Hence pixel replacement, which creates noticeable seams, is problematic. 2) Temporal coherency: successive frames should display smooth transitions. Hence frame-by-frame editing, which results in visual flicker, is inappropriate. Our work is aimed at providing an easy-to-use video editing tool that maximally satisfies the spatial and temporal constraints mentioned above and requires minimum user interaction. We propose a new framework for video editing in gradient domain. The spatio-temporal gradient fields of target videos are modified and/or mixed to generate a new gradient field which is usually not integrable. We propose a 3D video integration algorithm, which uses the variational method, to find the potential function whose gradient field is closest to the mixed gradient field in the sense of least squares. The video is reconstructed by solving a 3D Poisson equation. We derive an extension of current 2D gradient technique to 3D space, yielding in a novel video editing framework, which is very different from all current video editing software.",
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"abstract": "Radial distributions of turbulent kinetic energy k in the tank with different geometrical jet flows was predicted based on CFD method adopted RNG k - ε turbulent model. Some detailed information about turbulence kinetic energy and pressure was obtained. The results showed that at the plane of z=0, pressure did not change significantly and the value was small. With the increase of the distance x, the turbulent kinetic energy enlarged first and then reduced. Turbulent kinetic energy was suddenly reduced above the jet height at x=0. Under the same velocity and the nozzle clearance, the turbulent kinetic energy of flat -bottom was the smallest, and the turbulent kinetic energy of hemispherical-bottom was the largest. Under the same velocity, the pressure of hemispherical-bottom was the largest, wherever the pressure of flat-bottom was the smallest. The results can be theoretical basis for the optimum design and scale-up of the jet mixer.",
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"content": "Radial distributions of turbulent kinetic energy k in the tank with different geometrical jet flows was predicted based on CFD method adopted RNG k - ε turbulent model. Some detailed information about turbulence kinetic energy and pressure was obtained. The results showed that at the plane of z=0, pressure did not change significantly and the value was small. With the increase of the distance x, the turbulent kinetic energy enlarged first and then reduced. Turbulent kinetic energy was suddenly reduced above the jet height at x=0. Under the same velocity and the nozzle clearance, the turbulent kinetic energy of flat -bottom was the smallest, and the turbulent kinetic energy of hemispherical-bottom was the largest. Under the same velocity, the pressure of hemispherical-bottom was the largest, wherever the pressure of flat-bottom was the smallest. The results can be theoretical basis for the optimum design and scale-up of the jet mixer.",
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"abstract": "The contamination of ground and surface water with methyl tert-butyl ether (MTBE) has evoked substantial attention due to the frequent occurrence of storage tank leakage. Kinetic analysis of MTBE degradation by polyethyleneimine stabilized beads with alginate immobilized Methylibium petroleiphilum PM1 was investigated in this paper. The results showed that the stabilized beads could be used wider in respect to pH value, temperature and initial MTBE concentration than freely-suspended cells under the same conditions. The biochemical reaction rather than intraparticle diffusion resistance was considered as the key step by the kinetic analysis.",
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"content": "The contamination of ground and surface water with methyl tert-butyl ether (MTBE) has evoked substantial attention due to the frequent occurrence of storage tank leakage. Kinetic analysis of MTBE degradation by polyethyleneimine stabilized beads with alginate immobilized Methylibium petroleiphilum PM1 was investigated in this paper. The results showed that the stabilized beads could be used wider in respect to pH value, temperature and initial MTBE concentration than freely-suspended cells under the same conditions. The biochemical reaction rather than intraparticle diffusion resistance was considered as the key step by the kinetic analysis.",
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"fno": "3819b613",
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"Methylibium Petroleiphilum PM 1",
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"affiliation": null,
"fullName": "Dongzhi Chen",
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{
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"title": "2012 Third International Conference on Digital Manufacturing & Automation",
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"article": {
"id": "12OmNyQ7FDv",
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"content": "The progress of numerical simulation for high entrained gas-liquid two phase flow were summarized and analyzed. The results show that the Euler-Euler method is the most suitable for the air-water two-phase flow. The advantages and disadvantages of three multiphase flow models (VOF, Mixture and Eulerian model) are analyzed and the results show that the Eulerian model is most popular and accurate for simulation the strong turbulent air-water two-phase flow, especially needing to consider the conversion of continuous and discrete phase. The coupled Eulerian model and mixture turbulence model is the best choice for simulation the flow field on the strong air entrained flow in hydraulic engineering.",
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"normalizedAbstract": "The progress of numerical simulation for high entrained gas-liquid two phase flow were summarized and analyzed. The results show that the Euler-Euler method is the most suitable for the air-water two-phase flow. The advantages and disadvantages of three multiphase flow models (VOF, Mixture and Eulerian model) are analyzed and the results show that the Eulerian model is most popular and accurate for simulation the strong turbulent air-water two-phase flow, especially needing to consider the conversion of continuous and discrete phase. The coupled Eulerian model and mixture turbulence model is the best choice for simulation the flow field on the strong air entrained flow in hydraulic engineering.",
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"affiliation": null,
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"abstract": "Current out-of-distribution (OOD) detection benchmarks are commonly built by defining one dataset as in-distribution (ID) and all others as OOD. However, these benchmarks unfortunately introduce some unwanted and impractical goals, e.g., to perfectly distinguish CIFAR dogs from ImageNet dogs, even though they have the same semantics and negligible covariate shifts. These unrealistic goals will result in an extremely narrow range of model capabilities, greatly limiting their use in real applications. To overcome these drawbacks, we re-design the benchmarks and propose the semantically coherent out-of-distribution detection (SC-OOD). On the SC-OOD benchmarks, existing methods suffer from large performance degradation, suggesting that they are extremely sensitive to low-level discrepancy between data sources while ignoring their inherent semantics. To develop an effective SC-OOD detection approach, we leverage an external unlabeled set and design a concise framework featured by unsupervised dual grouping (UDG) for the joint modeling of ID and OOD data. The proposed UDG can not only enrich the semantic knowledge of the model by exploiting unlabeled data in an unsupervised manner, but also distinguish ID/OOD samples to enhance ID classification and OOD detection tasks simultaneously. Extensive experiments demonstrate that our approach achieves the state-of-the-art performance on SC-OOD benchmarks. Code and benchmarks are provided on our project page: https://jingkang50.github.io/projects/scood.",
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"content": "Since deep learning models have been implemented in many commercial applications, it is important to detect out-of-distribution (OOD) inputs correctly to maintain the performance of the models, ensure the quality of the collected data, and prevent the applications from being used for other-than-intended purposes. In this work, we propose a two-head deep convolutional neural network (CNN) and maximize the discrepancy between the two classifiers to detect OOD inputs. We train a two-head CNN consisting of one common feature extractor and two classifiers which have different decision boundaries but can classify in-distribution (ID) samples correctly. Unlike previous methods, we also utilize unlabeled data for unsupervised training and we use these unlabeled data to maximize the discrepancy between the decision boundaries of two classifiers to push OOD samples outside the manifold of the in-distribution (ID) samples, which enables us to detect OOD samples that are far from the support of the ID samples. Overall, our approach significantly outperforms other state-of-the-art methods on several OOD detection benchmarks and two cases of real-world simulation.",
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"abstract": "The use of optical techniques for the measurement and characterization of physical phenomena is of great interest in the scientific and industrial communities. This work shows the implementation of an integrated system for measuring Multi-angle Laser Light Scattering on a Field Programmable Gate Array. The embedded design delivers a compact and highly competitive system. The system is able to display, in real-time, the light intensity from a photo detector array showing in this way the corresponding pattern distribution of the scattered light at 16 sensing points. Some laser light scattering patterns from metallic samples with different roughness are shown.",
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"abstract": "Incorporating volumetric illumination into rendering of volumetric data increases visual realism, which can lead to improved spatial comprehension. It is known that spatial comprehension can be further improved by incorporating multiple light sources. However, many volumetric illumination algorithms have severe drawbacks when dealing with multiple light sources. These drawbacks are mainly high performance penalties and memory usage, which can be tackled with specialized data structures or data under sampling. In contrast, in this paper we present a method which enables volumetric illumination with multiple light sources without requiring precomputation or impacting visual quality. To achieve this goal, we introduce selective light updates which minimize the required computations when light settings are changed. We will discuss and analyze the novel concepts underlying selective light updates, and demonstrate them when applied to real-world data under different light settings.",
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"abstract": "Recovering the geometry of a human head from a single image, while factorizing the materials and illumination, is a severely ill-posed problem that requires prior information to be solved. Methods based on 3D Morphable Models (3DMM), and their combination with differentiable renderers, have shown promising results. However, the expressiveness of 3DMMs is limited, and they typically yield over-smoothed and identity-agnostic 3D shapes limited to the face region. Highly accurate full head reconstructions have recently been obtained with neural fields that parameterize the geometry using multilayer perceptrons. The versatility of these representations has also proved effective for disentangling geometry, materials and lighting. However, these methods require several tens of input images. In this paper, we introduce SIRA, a method which, from a single image, reconstructs human head avatars with high fidelity geometry and factorized lights and surface materials. Our key ingredients are two data-driven statistical models based on neural fields that resolve the ambiguities of single-view 3D surface reconstruction and appearance factorization. Experiments show that SIRA obtains state of the art results in 3D head reconstruction while at the same time it successfully disentangles the global illumination, and the diffuse and specular albedos. Furthermore, our reconstructions are amenable to physically-based appearance editing and head model relighting.",
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"content": "Recovering the geometry of a human head from a single image, while factorizing the materials and illumination, is a severely ill-posed problem that requires prior information to be solved. Methods based on 3D Morphable Models (3DMM), and their combination with differentiable renderers, have shown promising results. However, the expressiveness of 3DMMs is limited, and they typically yield over-smoothed and identity-agnostic 3D shapes limited to the face region. Highly accurate full head reconstructions have recently been obtained with neural fields that parameterize the geometry using multilayer perceptrons. The versatility of these representations has also proved effective for disentangling geometry, materials and lighting. However, these methods require several tens of input images. In this paper, we introduce SIRA, a method which, from a single image, reconstructs human head avatars with high fidelity geometry and factorized lights and surface materials. Our key ingredients are two data-driven statistical models based on neural fields that resolve the ambiguities of single-view 3D surface reconstruction and appearance factorization. Experiments show that SIRA obtains state of the art results in 3D head reconstruction while at the same time it successfully disentangles the global illumination, and the diffuse and specular albedos. Furthermore, our reconstructions are amenable to physically-based appearance editing and head model relighting.",
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"affiliation": "Universitat Politècnica de Catalunya",
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