We develop a framework for 3D shape and motion recovery of articulated deformable objects. We propose a formalism that incorporates the use of implicit surfaces into earlier robotics approaches that were designed to handle articulated structures. We demonstrate its effectiveness for human body modeling from synchronized video sequences. Our method is both robust and generic. It could easily be applied to other shape and motion recovery problems.

Optical motion capture provides an impressive ability to replicate gestures. However, even with a highly professional system there are many instances where crucial markers are occluded or when the algorithm confuses the trajectory of one marker with that of another. This requires much editing work on the part of the animator before the virtual characters are ready for their screen debuts. In this paper, we present an approach to increasing the robustness of a motion capture system by using a sophisticated anatomic human model. It includes a precise description of the skeleton's mobility and an approximated envelope. It allows us to accurately predict the 3-D location and visibility of markers, thus significantly increasing the robustness of marker tracking and assignment, and drastically reducing-or even eliminating-the need for human intervention during the 3D reconstruction process.

We show that we can effectively fit complex animation models to noisy image data. Our approach is based on robust least squares adjustment and takes advantage of three complementary sources of information: stereo data, silhouette edges and 2D feature points. We take stereo to be our main information source and use the other two whenever available. In this way, complete head models-including ears and hair-can be acquired with a cheap and entirely passive sensor, such as an ordinary video camera. The motion parameters of limbs can be similarly captured. They can then be fed to existing animation software to produce synthetic sequences.