Papers › Learning Pose Grammar for Monocular 3D Pose Estimation

Learning Pose Grammar for Monocular 3D Pose Estimation

1 Jun 2019IEEE Transactions on Pattern Analysis and Machine Intelligence 2019 6archive 2025-07-28

Yuanlu Xu, Wenguan Wang, Xiaobai Liu, Jianwen Xie, Song-Chun Zhu

In this paper, we propose a pose grammar to tackle the problem of 3D human pose estimation from a monocular RGB image. Our model takes estimated 2D pose as the input and learns a generalized 2D-3D mapping function to leverage into 3D pose. The proposed model consists of a base network which efficiently captures pose-aligned features and a hierarchy of Bi-directional RNNs (BRNNs) 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 data augmentation algorithm to further improve model robustness against appearance variations and cross-view generalization ability. 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.

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