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In multi-view human body capture systems, the recovered 3D geometry or eventhe acquired imagery data can be heavily corrupted due to occlusions, noise,limited field of- view, etc. Direct estimation of 3D pose, body shape or motionon these low-quality data has been traditionally challenging.In this paper, wepresent a graph-based non-rigid shape registration framework that cansimultaneously recover 3D human body geometry and estimate pose/motion at highfidelity.Our approach first generates a global full-body template byregistering all poses in the acquired motion sequence.We then construct adeformable graph by utilizing the rigid components in the global template. Wedirectly warp the global template graph back to each motion frame in order tofill in missing geometry. Specifically, we combine local rigidity and temporalcoherence constraints to maintain geometry and motion consistencies.Comprehensive experiments on various scenes show that our method is accurateand robust even in the presence of drastic motions.

Robust 3D Human Motion Reconstruction Via Dynamic Template Construction