Knowledge Commons of Institute of Automation,CAS
Progressive Bi-C3D Pose Grammar for Human Pose Estimation | |
Zhou Lu1,2![]() ![]() ![]() | |
2020 | |
会议名称 | Association for the Advancement of Artificial Intelligence |
会议日期 | 2.07-2.12 |
会议地点 | 纽约 |
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 |
收录类别 | EI |
七大方向——子方向分类 | 图像视频处理与分析 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44606 |
专题 | 紫东太初大模型研究中心 |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
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],2020. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Progressive Bi-C3D P(1531KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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