The article takes up the idea that disfluency markers can be classified along a continuum throughout the speech formulation process , going from the most discreet to the most prominent . It suggests that the more prominent the disfluency , the more likely is the visual channel to play a role ( interrupted eye contact and gesture production ) . As a consequence , models of gesture generation are a key challenge in research on virtual humans , embodied agents capable of face-to-face interaction with people . Machine learning approaches to gesture generation must take into account the conceptual content in utterances , physical properties of speech signals and the physical properties of the gestures themselves . To address this challenge , we proposed a gestural sign scheme to facilitate supervised learning and presented the DCNF model , a model to jointly learn deep neural networks and second order linear chain temporal contingency . Gesture sequence generation and gesture animation are the modules on which we have focused our main effort . Gesture sequence generation uses semantic concepts ( obtained from the semantic analysis ) associating them with several SSL gestures . This association is carried out based on a number of generation rules . Beyond the Here-And-Now : The Functions Gesture Serves . How Might Hearing Parents Foster Gesture Creation in Their Deaf Children ? Gesture Creation Across the Globe . We explore the use of a generative adversarial training paradigm to map speech to 3D gesture motion . We define the gesture generation problem as a series of smaller sub-problems , including plausible gesture dynamics , realistic joint configurations , and diverse and smooth motion . Each sub-problem is monitored by separate adversaries . It is required for designer to be skillful and innovative in order to create interesting and acceptable gestures . Gesture Recognition Design Toolkit ( GRDT ) is set of tools designed to simplify the gesture creation process for non experts . From designing a new gesture , to suggesting the best fit , to test it for false positive activation , till final selection , are the step-by-step functions of Gesture Recognition Design Toolkit . While the former strategy leads to incomplete test coverage , the latter suffers from efficiency issues . In this paper , we study the particular problem of gesture event generation for Android apps . We present a static analysis technique to obtain the gesture information : each UI component 's potentially relevant gestures , so as to reduce the amount of gesture events to be delivered in the automated testing . Gesture Recognition and Interpretation : 11 . A framework for gesture generation and interpretation J. Cassell 12 . Model-based interpreation of faces and hand gestures C. J. Taylor , A. Lanitis , T. F. Cootes , G. Edwards , and T. Ahmad 13 . The approach uses the power of rule-based system and inference engine to recognise and generate the interactive gestures . It filters the noise in recognition process as well as it also avoids the ambiguity of multiple solutions during gesture generation phase . We demonstrate the validity of this approach through kickboxing gestures of HOAP-2 robots , where they can respond to each other 's actions . We develop the scheme of action progress in our architecture to represent how far the action has been performed during its expected execution , and to instruct our model to synthesize actions with various paces . Our approach is evaluated on two challenging datasets for the task of gesture video generation . Experimental results show that our method can produce gesture videos with more realistic appearance and precise meaning than the state-of-the-art video generation approaches . Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics . Regarding the task of 3D shape representation , there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid , multi-view and point cloud . However , there is little effort on using mesh data in recent years , due to the complexity and irregularity of mesh data . The importance of the choice of this parameterization is that it facilitates the derivation of a 3D convolution equation for image formation under certain weak conditions . The problem of 3D shape and focused image reconstruction is formulated as an optimization problem where the di erence ( mean-square error ) between the observed image data and the estimated image data is minimized by an optimization approach . The estimated image data is obtained from the image sensing model and the current best known solutions to the 3D shape and focused image . They provide data compression capability , and some of the descriptors are invariant under scaling , rotation , and choice of the starting point for contour tracing . Several methods for 3D shape description also exist . However , they lack either the simplicity , the generality , or the data compression ability comparable to the 2D methods . We consider 3D shape description as a probability modeling problem . The local surface properties are first measured via various features , and then the probability density function ( pdf ) of the multidimensional feature vector becomes the shape descriptor . In this paper , I propose a list of such problems after a review of the current major 3D imaging modalities , and a description of the related medical needs . I then present some of the past and current work done in our research group EPIDAURE at INRIA , on the following topics : segmentation of 3D images ; 3D shape modelling ; 3D rigid and nonrigid registration ; 3D motion analysis ; and 3D simulation of therapy . Most topics are discussed in a synthetic manner , and illustrated by results . In this paper , a dataset , named 3DBodyTex , of static 3D body scans with high-quality texture information is presented along with a fully automatic method for body model fitting to a 3D scan . 3D shape modelling is a fundamental area of computer vision that has a wide range of applications in the industry . It is becoming even more important as 3D sensing technologies are entering consumer devices such as smartphones . We present a novel saliency-guided shading scheme for 3D shape depiction incorporating the mesh saliency into luminance enhancement in this paper . Using a distanced-based mesh saliency computation , we propose a new perceptual-saliency measure which can depict surface salient regions . This paper is strongly inspired by the observation in human vision and perception that a sudden change in the luminance plays a critical role to faithfully represent and recover the 3D information . In particular , we adopt the edge detection techniques in image processing for 3D shape visualization and present photic extremum lines ( PELs ) which emphasize significant variations of illumination over 3D surfaces . Comparing with the existing feature lines , PELs are more flexible and offer users more freedom to achieve desirable visualization effects . Throughout the course , connections will be made to common practices in the artistic depiction of 3D form . The course concludes with a discussion of how an understanding of human shape perception might be leveraged to enhance 3D shape visualization in photorealistic and non-photorealistic rendering . This course should be of interest to graphics researchers and practitioners who want to understand the portrayal of shape and , more broadly , anyone who is curious about human vision . Systems ( artificial or natural ) for visual object recognition are faced with three fundamental problems : the correspondence problem , the problem of representing 3D shape , and the problem of defining a robust similarity measure between images and views of objects . In this thesis , I address each of these problems : ( 1 ) I present a recognition algorithm ( RAST ) that works efficiently even when no correspondence or grouping information is given ; that is , it works in the presence of large amounts of clutter and with very primitive features . ( Desimone and Ungerleider , 1989 , p.268 ) . A number of recent works that do address the computational problem of representation tend to employ information-theoretic terms such as redundancy reduction and eecient coding This seminar series will survey and compare the diierent computational characterizations of the features of visual representation , and will discuss the relevance of the proposed approaches to the problem of representing 3D shape . Isometric deformations complicate 3D shape representation and recognition . Therefore , proper modeling is in need . document : SyncSpecCNN : Synchronized Spectral CNN for 3D Shape Segmentation In this paper , we study the problem of semantic annotation on 3D models that are represented as shape graphs . Along this track , most previous methods employ traditional machine learning techniques and construct classifiers based on geometric features . In the domain of unsupervised shape segmentation , there is one family of methods emphasizes the effectiveness of spectral analysis for 3D shape segmentation . Inspired by this , our framework aims to marry the powerfulness of deep neural network and spectral analysis for 3D shapes segmentation . subsection : Dataset For 3D shape segmentation task , we use a large scale shape part annotation dataset introduced by , which augments a subset of ShapeNet models with semantic part annotations . The dataset contains 16 categories of man - made shapes , with 2 to 6 parts per category . Inferring 3D shape from a single perspective is a fundamental human vision functionality but is extremely challenging for computer vision . Recently , great success has been achieved for 3d shape generation from a single color image using deep learning techniques . Taking advantage of convolutional layers on regular grids or multi - layer perception , the estimated 3D shape , as the output of the neural network , is represented as either a volume or point cloud . Much of this progress can be attributed to recent advances in artificial neural network research . Instead of the usual approach to representing 3D shapes with voxel occupancy vectors , promising recent work has taken to learning simple latent representations of such objects . Neural architectures that have been developed with this goal in mind include those based on deep belief networks , deep autoencoders , and 3D convolutional networks . To efficiently learn such a latent structure , we further exploit recent advances in approximate inference to develop a variational learning procedure . Empirically , we show that the learned generative model , which we call the Variational Shape Learner , acquires rich representations of 3D shapes that yield significantly improved performance across a multitude of 3D shape tasks . The main contributions of this paper are as follows : We propose a novel latent - variable model , which we call the Variational Shape Learner , which is capable of learning expressive feature representations of 3D shapes . section : Experiments To evaluate the quality of our proposed generative model for 3D shapes , we conduct several extensive experiments . In Section [ reference ] , we investigate our model section : Conclusion In this paper , we proposed the Variational Shape Learner , a hierarchical latent - variable model for 3D shape modelling , learnable through variational inference . In particular , we have demonstrated 3D shape generation results on a popular benchmark , the ModelNet dataset . We also used the learned embeddings of our model to obtain state - of - the - art in unsupervised shape classification and furthermore showed that we could generate unseen shapes using shape arithmetic . The mean mesh of each model under test is landmarked with the same 49 - point markup used in the dataset , and is registered against the ground truth mesh by performing a Procrustes alignment using the sparse annotations followed by Non - Rigid Iterative Closest Point ( N - ICP ) to iteratively deform the two surfaces until they are brought into correspondence . This provides a per - model ‘ ground - truth ’ for the 3D shape recovery problem for each image under test . Our error metric is the per - vertex dense error between the recovered shape and the model - specific corresponded ground - truth fit , normalized by the inter - ocular distance for the test mesh . They provide a set of 2D filters , which when convolved with input data , transform the data to informative features for higher level inference . In this paper , we focus on the problem of learning a 3D shape representation by a deep neural network . We keep two goals in mind when designing the network : the shape features should be discriminative for shape recognition and efficient for extraction at runtime . A general scheme could , in principle , be followed for both 2D and 3D discrete skeletonization . However , we will describe one approach for 2D skeletonization , mainly based on marking , in the distance transform , the shape elements that should be assigned to the skeleton , and another approach for 3D skeletonization , mainly based on iterated element removal . In both cases , the distance transform of the image will play a key role to obtain skeletons reflecting important shape features such as symmetry , elongation , and width . As often declared by customers , wearing glasses is a clear limiting factor for 3D adoption in the home . Autostereoscopic systems bring an interesting answer to this issue . As often declared by customers , wearing glasses is a clear limiting factor for 3D adoption in the home . Auto-stereoscopic systems bring an interesting answer to this issue . The effects of the modulation in two‐dimensional FSE imaging include ghosting and blurring artifacts and resolution loss in the phase‐encoding ( PE ) direction . In 3D FSE imaging , the use of two PE directions presents the opportunity for improved PE schemes . A new scheme for assignment of echoes to views in 3D FSE , termed generalized , has been developed . This scheme distributes T2 effects along both PE directions , allowing considerable flexibility in the selection of blurring artifact appearance . In a set of simulations , phantom experiments , and in vivo experiments , the performance of the generalized PE scheme for 3D FSE imaging was compared with the performance of existing PE schemes . The results demonstrate that the generalized PE scheme can be used to reduce blurring artifacts greatly relative to other PE techniques that are presently in use . Firstly we propose a practical method to calibrate underwater projectors with flat housings based on the pixel-wise varifocal model . Secondly we show a system for underwater 3D surface capture based on space carving principle [ 12 ] using multiple projectors and cameras in water . Thus , to speed up the unfolding process would have significant contribution to the field of CAD/CAM research . The paper begins by an extensive literature review on the status of methods , and the main concern would concentrates on how to design a fast algorithm for unfolding 3D surfaces . We paid more attention to improve a method that was proposed by McCartney in 1999 . ( Real time tracking of 3D objects applied to enhanced reality ) . Ce memoire de these a pour sujet le suivi temps reel d'objets en trois dimensions , dans le but de realiser des applications de realite augmentee . Despite the ease of access to low-cost sensors nowadays , knowledge of sensor integration and robotics middleware is still imperative to design successful robotic solutions . In this tutorial chapter , we provide an educational guide that leverages open source tools , such as the Robot Operating System ( ROS ) , and low-cost sensors , such as the Microsoft Kinect v2 , to design a full 3D indoor Mapping system . The ultimate goal of the system is to create a comprehensive and detailed 3D map of any indoor environment . The proposed algorithm combines the optimal convergence rate of the Bramble-Pasciak-Xu ( BPX ) preconditioner and a natural treatment of local refinement by the hierarchical basis multigrid ( HBM ) method . The BPX-HBM combination blends the best features of both methods and is applied to 3D magnetostatic scalar potential problems with first order tetrahedral elements . Adaptive local mesh refinement is incorporated in the overall computational process . For both , Decision Forests and Neural Networks , we present methods for implementation and a software package . Existing methods for 3D body estimation from images usually estimate the 14 most important , pose defining points in 2D and convert them to a 3D ‘ skeleton ’ . In this work we show that a carefully crafted energy function is sufficient to recover a full 3D body shape automatically from the keypoints . Unfortunately , inherent barrel distortion prevents accurate perception of range . This makes measurement and distance judgment difficult and causes difficulties in emerging applications , such as 3D medical-image registration . Such distortion also arises in other wide field-of-view camera circumstances . The generative models estimate the pose by finding the correspondences between the pre - defined body model and the input 3D point cloud . The ICP algorithm is commonly used for 3D body tracking [ reference ][ reference ][ reference ][ reference ] . Another method such as template fitting with Gaussian mixture models [ reference ] was also proposed . By contrast , the discriminative models do not require body templates and they directly estimate the positions of body joints . It does not estimate 3DMM parameters and , in fact , it completely bypasses the fitting of a 3DMM . Instead , our method directly produces a 3D volumetric representation of the facial geometry . Because of this fundamental difference , our method is also radically different in terms of the CNN architecture used : we used one that is able to make spatial predictions at a voxel level , as opposed to the networks of which holistically predict the 3DMM parameters . This parameter efficiency translates into lower training and segmentation times . For example 3D - UNet requires 3 days for training , whereas our network can be trained in approximately 17 hours using the same hardware . A limitation of this study comes from the fact that the ground - truth labels of test instances were not available to the participating teams , which makes interpretation of the results difficult . [ reference ] , which uses a 2D ConvNet descriptor to match local geometric features for mesh labeling . However , their approach operates only on synthetic and complete 3D models , while using ConvNets over input patches of concatenated feature vectors that do not have any kind of spatial correlation . In contrast , our work not only tackles the harder problem of matching real - world partial 3D data , but also properly leverages 3D ConvNets on volumetric data in a spatially coherent way . Recently , a number of papers have proposed to process point clouds directly without converting them to other formats . For example , proposed new types of deep net architectures , called PointNets , which have shown superior performance and efficiency in several 3D understanding tasks such as object classification and semantic segmentation . While PointNets are capable of classifying a whole point cloud or predicting a semantic class for each point in a point cloud , it is unclear how this architecture can be used for instance - level 3D object detection . These alignments factor out pose variations in data , and thus make 3D geometry pattern more evident , leading to an easier job of 3D learners . Second , learning in 3D space can better exploits the geometric and topological structure of 3D space . In principle , all objects live in 3D space ; therefore , we believe that many geometric structures , such as repetition , planarity , and symmetry , are more naturally parameterized and captured by learners that directly operate in 3D space . subsection : Training with Multi - task Losses We simultaneously optimize the three nets involved ( 3D instance segmentation PointNet , T - Net and amodal box estimation PointNet ) with multi - task losses ( as in Eq . [ reference ] ) . section : Conclusion We have presented an approach to extract 3D triangular meshes from singe images . We exploit the key advantages the mesh presentation can bring to us , and the key issues required to solve for success . Our framework provides a general means for optimizing probing locations in 3D fields where the gradients can be computed . We suspect this capability might be particularly important for analyzing 3D data with invisible internal structures . Moreover , our approach can easily be extended into higher dimensional fields , where a careful storage design of the input fields is important for making the I / O footprint tractable though . Hence , it is required to handle other facts involving these relations or entities in a probabilistic fashion . To do so , an increasingly popular method is to state the link prediction task as a 3D binary tensor completion problem , where each slice is the adjacency matrix of one relation type in the knowledge graph . Completion based on low - rank factorization or embeddings has been popularized with the Netflix challenge . Concurrently , progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing , namely deep generative models . Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics , e.g. , by the integration of differentiable rendering into network training . With a plethora of applications in computer graphics and vision , neural rendering is poised to become a new area in the graphics community , yet no survey of this emerging field exists .
The article takes up the idea that disfluency markers can be classified along a continuum throughout the speech formulation process , going from the most discreet to the most prominent . It suggests that the more prominent the disfluency , the more likely is the visual channel to play a role ( interrupted eye contact and gesture production ) .
As a consequence , models of gesture generation are a key challenge in research on virtual humans , embodied agents capable of face-to-face interaction with people . Machine learning approaches to gesture generation must take into account the conceptual content in utterances , physical properties of speech signals and the physical properties of the gestures themselves . To address this challenge , we proposed a gestural sign scheme to facilitate supervised learning and presented the DCNF model , a model to jointly learn deep neural networks and second order linear chain temporal contingency .
Gesture sequence generation and gesture animation are the modules on which we have focused our main effort . Gesture sequence generation uses semantic concepts ( obtained from the semantic analysis ) associating them with several SSL gestures . This association is carried out based on a number of generation rules .
Beyond the Here-And-Now : The Functions Gesture Serves . How Might Hearing Parents Foster Gesture Creation in Their Deaf Children ? Gesture Creation Across the Globe .
We explore the use of a generative adversarial training paradigm to map speech to 3D gesture motion . We define the gesture generation problem as a series of smaller sub-problems , including plausible gesture dynamics , realistic joint configurations , and diverse and smooth motion . Each sub-problem is monitored by separate adversaries .
It is required for designer to be skillful and innovative in order to create interesting and acceptable gestures . Gesture Recognition Design Toolkit ( GRDT ) is set of tools designed to simplify the gesture creation process for non experts . From designing a new gesture , to suggesting the best fit , to test it for false positive activation , till final selection , are the step-by-step functions of Gesture Recognition Design Toolkit .
While the former strategy leads to incomplete test coverage , the latter suffers from efficiency issues . In this paper , we study the particular problem of gesture event generation for Android apps . We present a static analysis technique to obtain the gesture information : each UI component 's potentially relevant gestures , so as to reduce the amount of gesture events to be delivered in the automated testing .
Gesture Recognition and Interpretation : 11 . A framework for gesture generation and interpretation J. Cassell 12 . Model-based interpreation of faces and hand gestures C. J. Taylor , A. Lanitis , T. F. Cootes , G. Edwards , and T. Ahmad 13 .
The approach uses the power of rule-based system and inference engine to recognise and generate the interactive gestures . It filters the noise in recognition process as well as it also avoids the ambiguity of multiple solutions during gesture generation phase . We demonstrate the validity of this approach through kickboxing gestures of HOAP-2 robots , where they can respond to each other 's actions .
We develop the scheme of action progress in our architecture to represent how far the action has been performed during its expected execution , and to instruct our model to synthesize actions with various paces . Our approach is evaluated on two challenging datasets for the task of gesture video generation . Experimental results show that our method can produce gesture videos with more realistic appearance and precise meaning than the state-of-the-art video generation approaches .
Mesh is an important and powerful type of data for 3D shapes and widely studied in the field of computer vision and computer graphics . Regarding the task of 3D shape representation , there have been extensive research efforts concentrating on how to represent 3D shapes well using volumetric grid , multi-view and point cloud . However , there is little effort on using mesh data in recent years , due to the complexity and irregularity of mesh data .
The importance of the choice of this parameterization is that it facilitates the derivation of a 3D convolution equation for image formation under certain weak conditions . The problem of 3D shape and focused image reconstruction is formulated as an optimization problem where the di erence ( mean-square error ) between the observed image data and the estimated image data is minimized by an optimization approach . The estimated image data is obtained from the image sensing model and the current best known solutions to the 3D shape and focused image .
They provide data compression capability , and some of the descriptors are invariant under scaling , rotation , and choice of the starting point for contour tracing . Several methods for 3D shape description also exist . However , they lack either the simplicity , the generality , or the data compression ability comparable to the 2D methods .
We consider 3D shape description as a probability modeling problem . The local surface properties are first measured via various features , and then the probability density function ( pdf ) of the multidimensional feature vector becomes the shape descriptor .
In this paper , I propose a list of such problems after a review of the current major 3D imaging modalities , and a description of the related medical needs . I then present some of the past and current work done in our research group EPIDAURE at INRIA , on the following topics : segmentation of 3D images ; 3D shape modelling ; 3D rigid and nonrigid registration ; 3D motion analysis ; and 3D simulation of therapy . Most topics are discussed in a synthetic manner , and illustrated by results .
In this paper , a dataset , named 3DBodyTex , of static 3D body scans with high-quality texture information is presented along with a fully automatic method for body model fitting to a 3D scan . 3D shape modelling is a fundamental area of computer vision that has a wide range of applications in the industry . It is becoming even more important as 3D sensing technologies are entering consumer devices such as smartphones .
We present a novel saliency-guided shading scheme for 3D shape depiction incorporating the mesh saliency into luminance enhancement in this paper . Using a distanced-based mesh saliency computation , we propose a new perceptual-saliency measure which can depict surface salient regions .
This paper is strongly inspired by the observation in human vision and perception that a sudden change in the luminance plays a critical role to faithfully represent and recover the 3D information . In particular , we adopt the edge detection techniques in image processing for 3D shape visualization and present photic extremum lines ( PELs ) which emphasize significant variations of illumination over 3D surfaces . Comparing with the existing feature lines , PELs are more flexible and offer users more freedom to achieve desirable visualization effects .
Throughout the course , connections will be made to common practices in the artistic depiction of 3D form . The course concludes with a discussion of how an understanding of human shape perception might be leveraged to enhance 3D shape visualization in photorealistic and non-photorealistic rendering . This course should be of interest to graphics researchers and practitioners who want to understand the portrayal of shape and , more broadly , anyone who is curious about human vision .
Systems ( artificial or natural ) for visual object recognition are faced with three fundamental problems : the correspondence problem , the problem of representing 3D shape , and the problem of defining a robust similarity measure between images and views of objects . In this thesis , I address each of these problems : ( 1 ) I present a recognition algorithm ( RAST ) that works efficiently even when no correspondence or grouping information is given ; that is , it works in the presence of large amounts of clutter and with very primitive features .
( Desimone and Ungerleider , 1989 , p.268 ) . A number of recent works that do address the computational problem of representation tend to employ information-theoretic terms such as redundancy reduction and eecient coding This seminar series will survey and compare the diierent computational characterizations of the features of visual representation , and will discuss the relevance of the proposed approaches to the problem of representing 3D shape .
Isometric deformations complicate 3D shape representation and recognition . Therefore , proper modeling is in need .
document : SyncSpecCNN : Synchronized Spectral CNN for 3D Shape Segmentation In this paper , we study the problem of semantic annotation on 3D models that are represented as shape graphs .
Along this track , most previous methods employ traditional machine learning techniques and construct classifiers based on geometric features . In the domain of unsupervised shape segmentation , there is one family of methods emphasizes the effectiveness of spectral analysis for 3D shape segmentation . Inspired by this , our framework aims to marry the powerfulness of deep neural network and spectral analysis for 3D shapes segmentation .
subsection : Dataset For 3D shape segmentation task , we use a large scale shape part annotation dataset introduced by , which augments a subset of ShapeNet models with semantic part annotations . The dataset contains 16 categories of man - made shapes , with 2 to 6 parts per category .
Inferring 3D shape from a single perspective is a fundamental human vision functionality but is extremely challenging for computer vision . Recently , great success has been achieved for 3d shape generation from a single color image using deep learning techniques . Taking advantage of convolutional layers on regular grids or multi - layer perception , the estimated 3D shape , as the output of the neural network , is represented as either a volume or point cloud .
Much of this progress can be attributed to recent advances in artificial neural network research . Instead of the usual approach to representing 3D shapes with voxel occupancy vectors , promising recent work has taken to learning simple latent representations of such objects . Neural architectures that have been developed with this goal in mind include those based on deep belief networks , deep autoencoders , and 3D convolutional networks .
To efficiently learn such a latent structure , we further exploit recent advances in approximate inference to develop a variational learning procedure . Empirically , we show that the learned generative model , which we call the Variational Shape Learner , acquires rich representations of 3D shapes that yield significantly improved performance across a multitude of 3D shape tasks . The main contributions of this paper are as follows : We propose a novel latent - variable model , which we call the Variational Shape Learner , which is capable of learning expressive feature representations of 3D shapes .
section : Experiments To evaluate the quality of our proposed generative model for 3D shapes , we conduct several extensive experiments . In Section [ reference ] , we investigate our model
section : Conclusion In this paper , we proposed the Variational Shape Learner , a hierarchical latent - variable model for 3D shape modelling , learnable through variational inference . In particular , we have demonstrated 3D shape generation results on a popular benchmark , the ModelNet dataset . We also used the learned embeddings of our model to obtain state - of - the - art in unsupervised shape classification and furthermore showed that we could generate unseen shapes using shape arithmetic .
The mean mesh of each model under test is landmarked with the same 49 - point markup used in the dataset , and is registered against the ground truth mesh by performing a Procrustes alignment using the sparse annotations followed by Non - Rigid Iterative Closest Point ( N - ICP ) to iteratively deform the two surfaces until they are brought into correspondence . This provides a per - model ‘ ground - truth ’ for the 3D shape recovery problem for each image under test . Our error metric is the per - vertex dense error between the recovered shape and the model - specific corresponded ground - truth fit , normalized by the inter - ocular distance for the test mesh .
They provide a set of 2D filters , which when convolved with input data , transform the data to informative features for higher level inference . In this paper , we focus on the problem of learning a 3D shape representation by a deep neural network . We keep two goals in mind when designing the network : the shape features should be discriminative for shape recognition and efficient for extraction at runtime .
A general scheme could , in principle , be followed for both 2D and 3D discrete skeletonization . However , we will describe one approach for 2D skeletonization , mainly based on marking , in the distance transform , the shape elements that should be assigned to the skeleton , and another approach for 3D skeletonization , mainly based on iterated element removal . In both cases , the distance transform of the image will play a key role to obtain skeletons reflecting important shape features such as symmetry , elongation , and width .
As often declared by customers , wearing glasses is a clear limiting factor for 3D adoption in the home . Autostereoscopic systems bring an interesting answer to this issue .
As often declared by customers , wearing glasses is a clear limiting factor for 3D adoption in the home . Auto-stereoscopic systems bring an interesting answer to this issue .
The effects of the modulation in two‐dimensional FSE imaging include ghosting and blurring artifacts and resolution loss in the phase‐encoding ( PE ) direction . In 3D FSE imaging , the use of two PE directions presents the opportunity for improved PE schemes . A new scheme for assignment of echoes to views in 3D FSE , termed generalized , has been developed . This scheme distributes T2 effects along both PE directions , allowing considerable flexibility in the selection of blurring artifact appearance . In a set of simulations , phantom experiments , and in vivo experiments , the performance of the generalized PE scheme for 3D FSE imaging was compared with the performance of existing PE schemes . The results demonstrate that the generalized PE scheme can be used to reduce blurring artifacts greatly relative to other PE techniques that are presently in use .
Firstly we propose a practical method to calibrate underwater projectors with flat housings based on the pixel-wise varifocal model . Secondly we show a system for underwater 3D surface capture based on space carving principle [ 12 ] using multiple projectors and cameras in water .
Thus , to speed up the unfolding process would have significant contribution to the field of CAD/CAM research . The paper begins by an extensive literature review on the status of methods , and the main concern would concentrates on how to design a fast algorithm for unfolding 3D surfaces . We paid more attention to improve a method that was proposed by McCartney in 1999 .
( Real time tracking of 3D objects applied to enhanced reality ) . Ce memoire de these a pour sujet le suivi temps reel d'objets en trois dimensions , dans le but de realiser des applications de realite augmentee .
Despite the ease of access to low-cost sensors nowadays , knowledge of sensor integration and robotics middleware is still imperative to design successful robotic solutions . In this tutorial chapter , we provide an educational guide that leverages open source tools , such as the Robot Operating System ( ROS ) , and low-cost sensors , such as the Microsoft Kinect v2 , to design a full 3D indoor Mapping system . The ultimate goal of the system is to create a comprehensive and detailed 3D map of any indoor environment .
The proposed algorithm combines the optimal convergence rate of the Bramble-Pasciak-Xu ( BPX ) preconditioner and a natural treatment of local refinement by the hierarchical basis multigrid ( HBM ) method . The BPX-HBM combination blends the best features of both methods and is applied to 3D magnetostatic scalar potential problems with first order tetrahedral elements . Adaptive local mesh refinement is incorporated in the overall computational process .
For both , Decision Forests and Neural Networks , we present methods for implementation and a software package . Existing methods for 3D body estimation from images usually estimate the 14 most important , pose defining points in 2D and convert them to a 3D ‘ skeleton ’ . In this work we show that a carefully crafted energy function is sufficient to recover a full 3D body shape automatically from the keypoints .
Unfortunately , inherent barrel distortion prevents accurate perception of range . This makes measurement and distance judgment difficult and causes difficulties in emerging applications , such as 3D medical-image registration . Such distortion also arises in other wide field-of-view camera circumstances .
The generative models estimate the pose by finding the correspondences between the pre - defined body model and the input 3D point cloud . The ICP algorithm is commonly used for 3D body tracking [ reference ][ reference ][ reference ][ reference ] . Another method such as template fitting with Gaussian mixture models [ reference ] was also proposed . By contrast , the discriminative models do not require body templates and they directly estimate the positions of body joints .
It does not estimate 3DMM parameters and , in fact , it completely bypasses the fitting of a 3DMM . Instead , our method directly produces a 3D volumetric representation of the facial geometry . Because of this fundamental difference , our method is also radically different in terms of the CNN architecture used : we used one that is able to make spatial predictions at a voxel level , as opposed to the networks of which holistically predict the 3DMM parameters .
This parameter efficiency translates into lower training and segmentation times . For example 3D - UNet requires 3 days for training , whereas our network can be trained in approximately 17 hours using the same hardware . A limitation of this study comes from the fact that the ground - truth labels of test instances were not available to the participating teams , which makes interpretation of the results difficult .
[ reference ] , which uses a 2D ConvNet descriptor to match local geometric features for mesh labeling . However , their approach operates only on synthetic and complete 3D models , while using ConvNets over input patches of concatenated feature vectors that do not have any kind of spatial correlation . In contrast , our work not only tackles the harder problem of matching real - world partial 3D data , but also properly leverages 3D ConvNets on volumetric data in a spatially coherent way .
Recently , a number of papers have proposed to process point clouds directly without converting them to other formats . For example , proposed new types of deep net architectures , called PointNets , which have shown superior performance and efficiency in several 3D understanding tasks such as object classification and semantic segmentation . While PointNets are capable of classifying a whole point cloud or predicting a semantic class for each point in a point cloud , it is unclear how this architecture can be used for instance - level 3D object detection .
These alignments factor out pose variations in data , and thus make 3D geometry pattern more evident , leading to an easier job of 3D learners . Second , learning in 3D space can better exploits the geometric and topological structure of 3D space . In principle , all objects live in 3D space ; therefore , we believe that many geometric structures , such as repetition , planarity , and symmetry , are more naturally parameterized and captured by learners that directly operate in 3D space .
subsection : Training with Multi - task Losses We simultaneously optimize the three nets involved ( 3D instance segmentation PointNet , T - Net and amodal box estimation PointNet ) with multi - task losses ( as in Eq . [ reference ] ) .
section : Conclusion We have presented an approach to extract 3D triangular meshes from singe images . We exploit the key advantages the mesh presentation can bring to us , and the key issues required to solve for success .
Our framework provides a general means for optimizing probing locations in 3D fields where the gradients can be computed . We suspect this capability might be particularly important for analyzing 3D data with invisible internal structures . Moreover , our approach can easily be extended into higher dimensional fields , where a careful storage design of the input fields is important for making the I / O footprint tractable though .
Hence , it is required to handle other facts involving these relations or entities in a probabilistic fashion . To do so , an increasingly popular method is to state the link prediction task as a 3D binary tensor completion problem , where each slice is the adjacency matrix of one relation type in the knowledge graph . Completion based on low - rank factorization or embeddings has been popularized with the Netflix challenge .
Concurrently , progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing , namely deep generative models . Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics , e.g. , by the integration of differentiable rendering into network training . With a plethora of applications in computer graphics and vision , neural rendering is poised to become a new area in the graphics community , yet no survey of this emerging field exists .
Disfluency markers and their facial and gestural correlates. preliminary observations on a dialogue in French Predicting Co-verbal Gestures: A Deep and Temporal Modeling Approach Proposing a speech to gesture translation architecture for Spanish deaf people The resilience of language : what gesture creation in deaf children can tell us about how all children learn language Multi-objective adversarial gesture generation A Gesture Recognition Design Toolkit Everyday Gesture Library Testing Android Apps via Guided Gesture Event Generation Computer Vision for Human–Machine Interaction: Acknowledgements Efficacy of gesture for communication among humanoid robots by fuzzy inference method Deep Gesture Video Generation With Learning on Regions of Interest MeshNet: Mesh Neural Network for 3D Shape Representation A unified approach to image focus and defocus analysis A generalized 3D shape sampling method and file format for storage or indexing Improving Efficiency of Density-Based Shape Descriptors for 3D Object Retrieval Medical computer vision, virtual reality and robotics 3DBodyTex: Textured 3D Body Dataset Saliency-Guided Luminance Enhancement for 3D Shape Depiction An Effective Illustrative Visualization Framework Based on Photic Extremum Lines (PELs) Visual perception of 3D shape Geometric Aspects of Visual Object Recognition Features of Visual Representation Modal function transformation for isometric 3D shape representation SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images Learning a Hierarchical Latent-Variable Model of 3D Shapes 3D Face Morphable Models "In-the-Wild" FPNN: Field Probing Neural Networks for 3D Data Discrete Skeletons from Distance Transforms in 2D and 3D Towards a free viewpoint and 3D intensity adjustment on multi-view display Display independent real-time multi-view rendering engine A generalized k‐sampling scheme for 3D fast spin echo Underwater 3D Surface Capture Using Multi-view Projectors and Cameras with Flat Housings Fast Flattening Algorithm for Non-developable 3D Surfaces Suivi temps-réel d'objets 3D pour la réalité augmentée. (Real time tracking of 3D objects applied to enhanced reality) A Guide for 3D Mapping with Low-Cost Sensors Using ROS Hierarchical basis multilevel preconditioners for 3D magnetostatic problems Combining Data-Driven 2D and 3D Human Appearance Models Nonlinear distortion correction in endoscopic video images V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation 3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions Frustum PointNets for 3D Object Detection from RGB-D Data Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images FPNN: Field Probing Neural Networks for 3D Data Complex Embeddings for Simple Link Prediction State of the Art on Neural Rendering
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DiSS IVA MIG APSEC AAAI ICIP MIRAGE 3DV 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV 2018 International Conference on 3D Vision (3DV) 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NIPS Medial Representations ICIP DSP MSV ICIP 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) Comput. Medical Imaging Graph. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ECCV NIPS ICML
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