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150
Scape: Shape completion and animation of people
- ACM Trans. Graph
, 2005
"... Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of ..."
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Cited by 285 (4 self)
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Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions
Human Computing and Machine Understanding of Human Behavior: A Survey
- SURVEY, PROC. ACM INT’L CONF. MULTIMODAL INTERFACES
, 2006
"... A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should b ..."
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Cited by 132 (33 self)
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A widely accepted prediction is that computing will move to the background, weaving itself into the fabric of our everyday living spaces and projecting the human user into the foreground. If this prediction is to come true, then next generation computing, which we will call human computing, should be about anticipatory user interfaces that should be human-centered, built for humans based on human models. They should transcend the traditional keyboard and mouse to include natural, human-like interactive functions including understanding and emulating certain human behaviors such as affective and social signaling. This article discusses a number of components of human behavior, how they might be integrated into computers, and how far we are from realizing the front end of human computing, that is, how far are we from enabling computers to understand human behavior.
Performance Animation from Low-dimensional Control Signals
- ACM Transactions on Graphics
, 2005
"... This paper introduces an approach to performance animation that employs video cameras and a small set of retro-reflective markers to create a low-cost, easy-to-use system that might someday be practical for home use. The low-dimensional control signals from the user's performance are supplement ..."
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Cited by 129 (18 self)
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This paper introduces an approach to performance animation that employs video cameras and a small set of retro-reflective markers to create a low-cost, easy-to-use system that might someday be practical for home use. The low-dimensional control signals from the user's performance are supplemented by a database of pre-recorded human motion. At run time, the system automatically learns a series of local models from a set of motion capture examples that are a close match to the marker locations captured by the cameras. These local models are then used to reconstruct the motion of the user as a full-body animation. We demonstrate the power of this approach with real-time control of six different behaviors using two video cameras and a small set of retro-reflective markers. We compare the resulting animation to animation from commercial motion capture equipment with a full set of markers.
The Correlated Correspondence Algorithm for Unsupervised Registration of Nonrigid Surfaces.
- In Proc. of Neural Information Processing Systems (NIPS).
, 2004
"... Figure 1 : Several frames from a motion animation generated by interpolating two scans of a puppet (far left and far right), which were automatically registered using the Correlated Correspondence algorithm. Abstract We present an unsupervised algorithm for registering 3D surface scans of a deforma ..."
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Cited by 113 (4 self)
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Figure 1 : Several frames from a motion animation generated by interpolating two scans of a puppet (far left and far right), which were automatically registered using the Correlated Correspondence algorithm. Abstract We present an unsupervised algorithm for registering 3D surface scans of a deformable object in very different configurations. Our algorithm does not use markers, nor does it assume prior knowledge about object shape, the dynamics of its deformation, or its alignment. The algorithm finds the correspondences between points in the two meshes using a joint probabilistic model over all point correspondences. This model combines preservation of local mesh geometry with more global constraints that capture the preservation of geodesic distance between corresponding pairs of points in the two meshes. Our approach successfully registers scans that exhibit large transformations, including both movement of articulate parts and non-rigid surface deformations. It applies even when one of the meshes is an incomplete range scan; thus, it can be used to automatically fill in the remaining surfaces for this partial scan, even if those surfaces were previously only seen in a different configuration. We also show how our results can be used to interpolate between two scans of a non-rigid object in a way that preserves surface geometry, leading to natural motion paths. Finally, we show that a registration of multiple scans in different configurations allows us to automatically identify components in articulate objects.
Shape-from-Silhouette Across Time - Part I: Theory and Algorithms
- International Journal of Computer Vision
, 2005
"... Shape-From-Silhouette (SFS) is a shape reconstruction method which constructs a 3D shape estimate of an object using silhouette images of the object. The output of a SFS algorithm is known as the Visual Hull (VH). Traditionally SFS is either performed on static objects, or separately at each time in ..."
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Cited by 107 (3 self)
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Shape-From-Silhouette (SFS) is a shape reconstruction method which constructs a 3D shape estimate of an object using silhouette images of the object. The output of a SFS algorithm is known as the Visual Hull (VH). Traditionally SFS is either performed on static objects, or separately at each time instant in the case of videos of moving objects. In this paper we develop a theory of performing SFS across time: estimating the shape of a dynamic object (with unknown motion) by combining all of the silhouette images of the object over time. We first introduce a one dimensional element called a Bounding Edge to represent the Visual Hull. We then show that aligning two Visual Hulls using just their silhouettes is in general ambiguous and derive the geometric constraints (in terms of Bounding Edges) that govern the alignment. To break the alignment ambiguity, we combine stereo information with silhouette information and derive a Temporal SFS algorithm which consists of two steps: (1) estimate the motion of the objects over time (Visual Hull Alignment) and (2) combine the silhouette information using the estimated motion (Visual Hull Refinement). The algorithm is first developed for rigid objects and then extended to articulated objects. In the Part II of this paper we apply our temporal SFS algorithm to two human-related applications: (1) the acquisition of detailed human kinematic models and (2) marker-less motion tracking.
Fast multiple object tracking via a hierarchical particle filter
- In The IEEE International Conference on Computer Vision (ICCV
, 2005
"... A very efficient and robust visual object tracking algo-rithm based on the particle filter is presented. The method characterizes the tracked objects using color and edge ori-entation histogram features. While the use of more features and samples can improve the robustness, the computational load re ..."
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Cited by 92 (4 self)
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A very efficient and robust visual object tracking algo-rithm based on the particle filter is presented. The method characterizes the tracked objects using color and edge ori-entation histogram features. While the use of more features and samples can improve the robustness, the computational load required by the particle filter increases. To acceler-ate the algorithm while retaining robustness we adopt sev-eral enhancements in the algorithm. The first is the use of integral images [34] for efficiently computing the color fea-tures and edge orientation histograms, which allows a large amount of particles and a better description of the targets. Next, the observation likelihood based on multiple features is computed in a coarse-to-fine manner, which allows the computation to quickly focus on the more promising regions. Quasi-random sampling of the particles allows the filter to achieve a higher convergence rate. The resulting tracking algorithm maintains multiple hypotheses and offers robust-ness against clutter or short period occlusions. Experimen-tal results demonstrate the efficiency and effectiveness of the algorithm for single and multiple object tracking. 1
Recognizing human actions using multiple features, CVPR
, 2008
"... In this paper, we propose a framework that fuses multiple features for improved action recognition in videos. The fusion of multiple features is important for recognizing actions as often a single feature based representation is not enough to capture the imaging variations (view-point, illumination ..."
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Cited by 77 (7 self)
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In this paper, we propose a framework that fuses multiple features for improved action recognition in videos. The fusion of multiple features is important for recognizing actions as often a single feature based representation is not enough to capture the imaging variations (view-point, illumination etc.) and attributes of individuals (size, age, gender etc.). Hence, we use two types of features: i) a quantized vocabulary of local spatio-temporal (ST) volumes (or cuboids), and ii) a quantized vocabulary of spin-images, which aims to capture the shape deformation of the actor by considering actions as 3D objects (x,y,t). To optimally combine these features, we treat different features as nodes in a graph, where weighted edges between the nodes represent the strength of the relationship between entities. The graph is then embedded into a k-dimensional space subject to the criteria that similar nodes have Euclidian coordinates which are closer to each other. This is achieved by converting this constraint into a minimization problem whose solution is the eigenvectors of the graph Laplacian matrix. This procedure is known as Fiedler Embedding. The performance of the proposed framework is tested on publicly available data sets. The results demonstrate that fusion of multiple features helps in achieving improved performance, and allows retrieval of meaningful features and videos from the embedding space. 1.
Illumination normalization with time-dependent intrinsic images for video surveillance
- In Proc. of IEEE Int’l Conf. on Computer Vision and Pattern Recognition, 2004
, 2003
"... Cast shadows produce troublesome effects for video surveillance systems, typically for object tracking from a fixed viewpoint, since it yields appearance variations of objects depending on whether they are inside or outside the shadow. To robustly eliminate these shadows from image sequences as a pr ..."
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Cited by 52 (3 self)
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Cast shadows produce troublesome effects for video surveillance systems, typically for object tracking from a fixed viewpoint, since it yields appearance variations of objects depending on whether they are inside or outside the shadow. To robustly eliminate these shadows from image sequences as a preprocessing stage for robust video surveillance, we propose a framework based on the idea of intrinsic images. Unlike previous methods for deriving intrinsic images, we derive time-varying reflectance images and corresponding illumination images from a sequence of images. Using obtained illumination images, we normalize the input image sequence in terms of incident lighting distribution to eliminate shadow effects. We also propose an illumination normalization scheme which can potentially run in real time, utilizing the illumination eigenspace, which captures the illumination variation due to weather, time of day etc., and a shadow interpolation method based on shadow hulls. This paper describes the theory of the framework with simulation results, and shows its effectiveness with object tracking results on real scene data sets for traffic monitoring. 1
Efficient mean-shift tracking via a new similarity measure
- in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ’05
, 2005
"... The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the model and target image. The most typically used similarity measures are th ..."
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Cited by 52 (4 self)
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The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the model and target image. The most typically used similarity measures are the Bhattacharyya coefficient or the Kullback-Leibler divergence. In practice, these approaches face three difficulties. First, the spatial information of the target is lost when the color histogram is employed, which precludes the application of more elaborate motion models. Second, the classical similarity measures are not very discriminative. Third, the sample-based classical similarity measures require a calculation that is quadratic in the number of samples, making real-time performance difficult. To deal with these difficulties we propose a new, simple-tocompute and more discriminative similarity measure in spatial-feature spaces. The new similarity measure allows the mean shift algorithm to track more general motion models in an integrated way. To reduce the complexity of the computation to linear order we employ the recently proposed improved fast Gauss transform. This leads to a very efficient and robust nonparametric spatial-feature tracking algorithm. The algorithm is tested on several image sequences and shown to achieve robust and reliable frame-rate tracking.
