Unsupervised 3D Articulated Object Correspondences with Part Approximation and Shape Refinement

Reconstructing 3D human shapes with high-quality geometry as well as dense correspondences is important for many applications. Template fitting based methods can generate meshes with desired requirements but have difficulty in capturing high-quality details and accurate poses. The main challenge lies in the models have apparent discrepancies in different poses. Directly learning large-scale displacement of each point to account for different posed shapes is prone to artifacts and does not generalize well. Statistic representation based methods, can avoid artifacts by restricting human shapes to a limited shape expression space, which also makes it difficult to produce shape details. In this work, we propose a coarse-to-fine method to address the problem by dividing it into part approximation and shape refinement in an unsupervised manner. Our basic observation is that the poses of human parts account for most articulated shape variations and benefit pose generalization. Moreover, geometry details can be easily fitted once the part poses are estimated. At the coarse-fitting stage, we propose a part approximation network, to transform a template to fit inputs by a set of pose parameters. For refinement, we propose a shape refinement network, to fit shape details. Qualitative and quantitative studies on several datasets demonstrate that our method performs better than other unsupervised methods.