This paper proposes a method for capturing the performance of a human or an animal from a multi-view video sequence. Given an articulated template model and silhouettes from a multi-view image sequence, our approach recovers not only the movement of the skeleton, but also the possibly non-rigid temporal deformation of the 3D surface. While large scale deformations or fast movements are captured by the skeleton pose and approximate surface skinning, true small scale deformations or non-rigid garment motion are captured by fitting the surface to the silhouette. We further propose a novel optimization scheme for skeleton-based pose estimation that exploits the skeleton’s tree structure to split the optimization problem into a local one and a lower dimensional global one. We show on various sequences that our approach can capture the 3D motion of animals and humans accurately even in the case of rapid movements and wide apparel like skirts. 1.

Abstract. This paper presents a method of image processing used in a mono-vision system in order to study semiotic gestures. We present a robust method to track the hands and face of a person performing gestural communication and the Signs ’ language communication. A model of skin is used to compute the observation density as a skin colour distribution in the image. Three particle filter trackers are implemented, with re-sampling and annealed update steps to increase their robustness to occultation and high acceleration variations of body parts’. Evaluations of the trackers with and without these enhancements, show the improvement that they bring. 1

For model-based 3D human pose estimation, even simple models of the human body lead to high-dimensional state spaces. Where the class of activity is known a priori, lowdimensional activity models learned from training data make possible a thorough and efficient search for the best pose. Conversely, searching for solutions in the full state space places no restriction on the class of motion to be recovered, but is both difficult and expensive. This paper explores a potential middle ground between these approaches, using the hierarchical Gaussian process latent variable model to learn activity at different hierarchical scales within the human skeleton. We show that by training on full-body activity data then descending through the hierarchy in stages and exploring subtrees independently of one another, novel poses may be recovered. Experimental results on motion capture data and monocular video sequences demonstrate the utility of the approach, and comparisons are drawn with existing low-dimensional activity models.

This research is partially funded by the Max-Planck Center for Visual Computing and Communication. We would like to thank Leonid Sigal and Stefano Tracking of human motion in video is usually tackled either by local optimization or filtering approaches. While local optimization offers accurate estimates but often looses track due to local optima, particle filtering can recover from errors at the expense of a poor accuracy due to overestimation of noise. In this paper, we propose to embed global stochastic optimization in a tracking framework. This new optimization technique exhibits both the robustness of filtering strategies and a remarkable accuracy. We apply the optimization to an energy function that relies on silhouettes and color, as well as some prior information on physical constraints. This framework provides a general solution to markerless human motion capture since neither excessive preprocessing nor strong assumptions except of a 3D model are required. The optimization provides initialization and accurate tracking even

comparison of 3d model-based tracking approaches for human motion capture

The presented article explains an innovating method to process a computer-aided segmentation of sign language sentences. After having tracked the signers hands in a video, the traitment consists in detecting motion attributes such as repetition or symmetries. Those observations are taken into account to process a gesture segmentation. We also discuss about the evaluation of such a segmentation. 1.

Abstract not found

Abstract not found