TL;DR
MuPPet introduces a multi-person 2D-to-3D pose lifting framework that explicitly models inter-person relationships, significantly improving accuracy and robustness in social interaction scenarios.
Contribution
It presents novel components like Person Encoding, Permutation Augmentation, and Dynamic Multi-Person Attention to effectively model inter-person correlations.
Findings
Outperforms state-of-the-art methods on group interaction datasets.
Enhances robustness in occlusion scenarios.
Highlights the importance of inter-person correlation modeling.
Abstract
Multi-person social interactions are inherently built on coherence and relationships among all individuals within the group, making multi-person localization and body pose estimation essential to understanding these social dynamics. One promising approach is 2D-to-3D pose lifting which provides a 3D human pose consisting of rich spatial details by building on the significant advances in 2D pose estimation. However, the existing 2D-to-3D pose lifting methods often neglect inter-person relationships or cannot handle varying group sizes, limiting their effectiveness in multi-person settings. We propose MuPPet, a novel multi-person 2D-to-3D pose lifting framework that explicitly models inter-person correlations. To leverage these inter-person dependencies, our approach introduces Person Encoding to structure individual representations, Permutation Augmentation to enhance training diversity,…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
