Knowledge Commons of Institute of Automation,CAS
OsGG-Net: One-step Graph Generation Network for Unbiased Head Pose Estimation | |
Shentong Mo2; Xin M(辛淼)1 | |
2021 | |
会议名称 | 29th ACM International Conference on Multimedia |
会议日期 | October 20, 2021 - October 24, 2021 |
会议地点 | Chengdu, China |
摘要 | Head pose estimation is a crucial problem that involves the prediction of the Euler angles of a human head in an image. Previous approaches predict head poses through landmarks detection, which can be applied to multiple downstream tasks. However, previous landmark-based methods can not achieve comparable performance to the current landmark-free methods due to lack of modeling the complex nonlinear relationships between the geometric distribution of landmarks and head poses. Another reason for the performance bottleneck is that there exists biased underlying distribution of the 3D pose angles in the current head pose benchmarks. In this work, we propose OsGG-Net, a One-step Graph Generation Network for estimating head poses from a single image by generating a landmark-connection graph to model the 3D angle associated with the landmark distribution robustly. To further ease the angle-biased issues caused by the biased data distribution in learning the graph structure, we propose the UnBiased Head Pose Dataset, called UBHPD, and a new unbiased metric, namely UBMAE, for unbiased head pose estimation. We conduct extensive experiments on various benchmarks and UBHPD where our method achieves the state-of-the-art results in terms of the commonly-used MAE metric and our proposed UBMAE. Comprehensive ablation studies also demonstrate the effectiveness of each part in our approach. |
关键词 | Graph Generation Unbiased Head Pose Estimation |
DOI | 10.1145/3474085.3475417 |
收录类别 | EI |
语种 | 英语 |
EI入藏号 | 20214711200064 |
七大方向——子方向分类 | 智能交互 |
国重实验室规划方向分类 | 人机混合智能 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51490 |
专题 | 复杂系统认知与决策实验室 复杂系统认知与决策实验室_高效智能计算与学习 |
通讯作者 | Xin M(辛淼) |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.Carnegie Mellon University |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Shentong Mo,Xin M. OsGG-Net: One-step Graph Generation Network for Unbiased Head Pose Estimation[C],2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
OsGG-Net One-step Gr(1865KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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