Learning Pose Grammar to Encode Human Body Configuration for 3D Pose Estimation

In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation. Our model directly takes 2D pose as input and learns a generalized 2D-3D mapping function. The proposed model consists of a base network which efficiently captures pose-aligned features and a hierarchy of Bi-directional RNNs (BRNN) on the top to explicitly incorporate a set of knowledge regarding human body configuration (i. e. , kinematics, symmetry, motor coordination). The proposed model thus enforces high-level constraints over human poses. In learning, we develop a pose sample simulator to augment training samples in virtual camera views, which further improves our model generalizability. We validate our method on public 3D human pose benchmarks and propose a new evaluation protocol working on cross-view setting to verify the generalization capability of different methods. We empirically observe that most state-of-the-art methods encounter difficulty under such setting while our method can well handle such challenges.

Authors

• Hao-Shu Fang
• Yuanlu Xu Meta Reality Labs Research
• Wenguan Wang Zhejiang University
• Xiaobai Liu
• Song-Chun Zhu State Key Laboratory of General Artificial Intelligence, BIGAI

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