We describe an end-to-end method for recovering 3D human body mesh from
single images and monocular videos. Different from the existing methods try to
obtain all the complex 3D pose, shape, and camera parameters from one coupling
feature, we propose a skeleton-disentangling based framework, which divides
this task into multi-level spatial and temporal granularity in a decoupling
manner. In spatial, we propose an effective and pluggable "disentangling the
skeleton from the details" (DSD) module. It reduces the complexity and
decouples the skeleton, which lays a good foundation for temporal modeling. In
temporal, the self-attention based temporal convolution network is proposed to
efficiently exploit the short and long-term temporal cues. Furthermore, an
unsupervised adversarial training strategy, temporal shuffles and order
recovery, is designed to promote the learning of motion dynamics. The proposed
method outperforms the state-of-the-art 3D human mesh recovery methods by 15.4%
MPJPE and 23.8% PA-MPJPE on Human3.6M. State-of-the-art results are also
achieved on the 3D pose in the wild (3DPW) dataset without any fine-tuning.
Especially, ablation studies demonstrate that skeleton-disentangled
representation is crucial for better temporal modeling and generalization.

提出了一种骨架解耦的 3D 人体网格恢复方法，该方法利用自我注意力机制实现多级空间和时间粒度的分离建模，并通过无监督对抗训练策略以及临时混洗和顺序恢复，成功提高了人体网格恢复的精度。