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