  Even though Non-rigid Structure-from-Motion (NRSfM) has been extensively
studied and great progress has been made, there are still key challenges that
hinder their broad real-world applications: 1) the inherent motion/rotation
ambiguity requires either explicit camera motion recovery with extra constraint
or complex Procrustean Alignment; 2) existing low-rank modeling of the global
shape can over-penalize drastic deformations in the 3D shape sequence. This
paper proposes to resolve the above issues from a spatial-temporal modeling
perspective. First, we propose a novel Temporally-smooth Procrustean Alignment
module that estimates 3D deforming shapes and adjusts the camera motion by
aligning the 3D shape sequence consecutively. Our new alignment module remedies
the requirement of complex reference 3D shape during alignment, which is more
conductive to non-isotropic deformation modeling. Second, we propose a
spatial-weighted approach to enforce the low-rank constraint adaptively at
different locations to accommodate drastic spatially-variant deformation
reconstruction better. Our modeling outperform existing low-rank based methods,
and extensive experiments across different datasets validate the effectiveness
of our method.
