Progressive Bi-C3D Pose Grammar for Human Pose Estimation | |
Zhou Lu1,2; Chen Yingying1,2; Wang Jinqiao1,2; Lu Hanqing1,2 | |
2020 | |
会议日期 | 2.07-2.12 |
会议地点 | 纽约 |
英文摘要 | 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 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/44606] ![]() |
专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
作者单位 | 1.University of Chinese Academy of Sciences 2.Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Zhou Lu,Chen Yingying,Wang Jinqiao,et al. Progressive Bi-C3D Pose Grammar for Human Pose Estimation[C]. 见:. 纽约. 2.07-2.12. |
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