PandaPose: 3D Human Pose Lifting from a Single Image via Propagating 2D Pose Prior to 3D Anchor Space

Jinghong Zheng, Changlong Jiang, Yang Xiao, Jiaqi Li, Haohong Kuang, Hang Xu, Ran Wang, Zhiguo Cao, Min Du, Joey Tianyi Zhou

Keywords: Human pose estimation, anchor to joint prediction, depth estimation

Abstract:

3D human pose lifting from a single RGB image is a challenging task in 3D vision. Existing methods typically establish a direct joint-to-joint mapping from 2D to 3D poses based on 2D features. This formulation suffers from two fundamental limitations: inevitable error propagation from input predicted 2D pose to 3D predictions and inherent difficulties in handling self-occlusion cases.
In this paper, we propose PandaPose, a 3D human pose lifting approach via propagating 2D pose prior to 3D anchor space as the unified intermediate representation. Specifically, our 3D anchor space comprises:
(1) Joint-wise 3D anchors in the canonical coordinate system, providing accurate and robust priors to mitigate 2D pose estimation inaccuracies.
(2) Depth-aware joint-wise feature lifting that hierarchically integrates depth information to resolve self-occlusion ambiguities.
(3) The anchor-feature interaction decoder that incorporates 3D anchors with lifted features to generate unified anchor queries encapsulating joint-wise 3D anchor set, visual cues and geometric depth information.
The anchor queries are further employed to facilitate anchor-to-joint ensemble prediction.
Experiments on three well-established benchmarks (i.e., Human3.6M, MPI-INF-3DHP and 3DPW) demonstrate the superiority of our proposition.
The substantial reduction in error by 14.7% compared to SOTA methods
on the challenging conditions of Human3.6M and qualitative comparisons further showcase the effectiveness and robustness of our approach.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 14056