Progressive Hypothesis Transformer for 3D Human Mesh Recovery
Part Of
Proceedings - 2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
Journal Volume
34
Start Page
6311
End Page
6320
ISBN (of the container)
979-835031892-0
Date Issued
2024-01-03
Author(s)
Abstract
Recent advancements in Transformer-based human mesh reconstruction (HMR) are commendable. However, these models often lift 2D images directly to 3D vertices without explicit intermediate guidance. In addition, the global attention mechanism tends to spread attention across larger body areas and even unrelated background regions during human mesh estimation, rather than focusing on critical local regions such as human body joints. This tendency leads to inaccurate and unrealistic results for complex activities. To address these challenges, we introduce the Progressive Hypothesis Transformer, which employs 2D and 3D pose predictions to progressively guide our model. Moreover, we propose a mechanism that generates multiple plausible hypotheses for both 2D and 3D poses to mitigate potential inaccuracies arising from intermediate pose estimations. Our model also incorporates inter-intra attention to capture correlations between joints and hypotheses. Experimental results demonstrate that our method surpasses existing image-based approaches on Human3.6M [13] and 3DPW [36] with fewer parameters and relatively lower computational costs.
Event(s)
2024 IEEE Winter Conference on Applications of Computer Vision, WACV 2024
Publisher
IEEE
Type
conference paper