High-quality nonintrusive human motion capture is necessary for acquisition of model-based free-viewpoint video of human actors. Silhouette-based approaches have demonstrated that they are able to accurately recover a large range of human motion from multiview video. However, they fail to make use of all available information, specifically that of texture information. This paper presents an algorithm that uses motion fields constructed from optical flow in multiview video sequences. The use of motion fields augments the silhouette-based method by incorporating texture-information into the tracking process. The algorithm is a key-component in a larger free-viewpoint video system of human actors. Our results demonstrate that our method accurately estimates pose parameters and allows for realistic texture generation in 3D video sequences.

In free-viewpoint video, the viewer can interactively choose his viewpoint in 3-D space to observe the action of a dynamic real-world scene from arbitrary perspectives. The human body and its motion plays a central role in most visual media and its structure can be exploited for robust motion estimation and efficient visualization. This paper describes a system that uses multi-view synchronized video footage of an actor's performance to estimate motion parameters and to interactively re-render the actor's appearance from any viewpoint.The actor's silhouettes are extracted from synchronized video frames via background segmentation and then used to determine a sequence of poses for a 3D human body model. By employing multi-view texturing during rendering, time-dependent changes in the body surface are reproduced in high detail. The motion capture subsystem runs offline, is non-intrusive, yields robust motion parameter estimates, and can cope with a broad range of motion. The rendering subsystem runs at real-time frame rates using ubiquous graphics hardware, yielding a highly naturalistic impression of the actor. The actor can be placed in virtual environments to create composite dynamic scenes. Free-viewpoint video allows the creation of camera fly-throughs or viewing the action interactively from arbitrary perspectives.

We present a new algorithm for extracting adaptive multiresolution triangle meshes from volume datasets. The algorithm guarantees that the topological genus of the generated mesh is the same as the genus of the surface embedded in the volume dataset at all levels of detail. In addition to this "hard constraint" on the genus of the mesh, the user can choose to specify some number of soft geometric constraints, such as triangle aspect ratio, minimum or maximum total number of vertices, minimum and/or maximum triangle edge lengths, maximum magnitude of various error metrics per triangle or vertex, including maximum curvature (area) error, maximum distance to the surface, and others. The mesh extraction process is fully automatic and does not require manual adjusting of parameters to produce the desired results as long as the user does not specify incompatible constraints. The algorithm robustly handles special topological cases, such as trimmed surfaces (intersections of the surface with the volume boundary), and manifolds with multiple disconnected components (several closed surfaces embedded in the same volume dataset). The meshes may self-intersect at coarse resolutions. However, the self-intersections are corrected automatically as the resolution of the meshes increase. We show several examples of meshes extracted from complex volume datasets.