Tracking human joint motion for turntable-based static model reconstruction

We propose a method that makes standard turntable-based vision acquisition a practical method for recovering models of human geometry. A human subject typically exhibits some unintended joint motion while rotating on a turntable. Ignoring such motion causes shape-from-silhouette to excessively carve the model, resulting in loss of geometry (especially on limbs). We utilize silhouette cues with an initial automatically recovered skinned-model to recover this joint motion, or wobbling. The recovered joint motion gives the calibration of each rigid body of the subject, allowing for temporal fusion of image cues (e.g., silhouettes and texture) used to refine the geometry. Our method gives improved results on real data sets when considering silhouette overlap in novel views. The recovered geometry is useful in vision tasks such as multi-view image-based tracking of humans, where the recent trend of using a priori laser-scanned geometry could be replaced with a more cost effective vision-based geometry.