TY - JOUR AU - Zhou, Lu AU - Chen, Yingying AU - Wang, Jinqiao AU - Lu, Hanqing PY - 2020/04/03 Y2 - 2024/03/28 TI - Progressive Bi-C3D Pose Grammar for Human Pose Estimation JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 34 IS - 07 SE - AAAI Technical Track: Vision DO - 10.1609/aaai.v34i07.7004 UR - https://ojs.aaai.org/index.php/AAAI/article/view/7004 SP - 13033-13040 AB - <p>In this paper, we propose a progressive pose grammar network learned with Bi-C3D (Bidirectional Convolutional 3D) for human pose estimation. Exploiting the dependencies among the human body parts proves effective in solving the problems such as complex articulation, occlusion and so on. Therefore, we propose two articulated grammars learned with Bi-C3D to build the relationships of the human joints and exploit the contextual information of human body structure. Firstly, a local multi-scale Bi-C3D kinematics grammar is proposed to promote the message passing process among the locally related joints. The multi-scale kinematics grammar excavates different levels human context learned by the network. Moreover, a global sequential grammar is put forward to capture the long-range dependencies among the human body joints. The whole procedure can be regarded as a local-global progressive refinement process. Without bells and whistles, our method achieves competitive performance on both MPII and LSP benchmarks compared with previous methods, which confirms the feasibility and effectiveness of C3D in information interactions.</p> ER -