咨询与建议

看过本文的还看了

相关文献

该作者的其他文献

文献详情 >Context modeling in 3D human p... 收藏
arXiv

Context modeling in 3D human pose estimation: A unified perspective

作     者:Ma, Xiaoxuan Su, Jiajun Wang, Chunyu Ci, Hai Wang, Yizhou 

作者机构:Dept. of Computer Science Center on Frontiers of Computing Studies Peking University China Center for Data Science Adv. Inst. of Info. Tech. Peking University China  Beijing Film Academy China Microsoft Research Asia Deepwise AI Lab 

出 版 物:《arXiv》 (arXiv)

年 卷 期:2021年

核心收录:

主  题:Graph neural networks 

摘      要:Estimating 3D human pose from a single image suffers from severe ambiguity since multiple 3D joint configurations may have the same 2D projection. The state-of-the-art methods often rely on context modeling methods such as pictorial structure model (PSM) or graph neural network (GNN) to reduce ambiguity. However, there is no study that rigorously compares them side by side. So we first present a general formula for context modeling in which both PSM and GNN are its special cases. By comparing the two methods, we found that the end-to-end training scheme in GNN and the limb length constraints in PSM are two complementary factors to improve results. To combine their advantages, we propose ContextPose based on attention mechanism that allows enforcing soft limb length constraints in a deep network. The approach effectively reduces the chance of getting absurd 3D pose estimates with incorrect limb lengths and achieves state-of-the-art results on two benchmark datasets. More importantly, the introduction of limb length constraints into deep networks enables the approach to achieve much better generalization performance. Copyright © 2021, The Authors. All rights reserved.

读者评论 与其他读者分享你的观点

用户名:未登录
我的评分