ProGraph: Temporally-alignable Probability Guided Graph Topological Modeling for 3D Human Reconstruction

TL;DR

ProGraph introduces a novel graph topological modeling approach with probability guidance and temporal alignment to improve 3D human reconstruction from monocular videos, especially under occlusions and blurriness.

Contribution

It proposes a topological-aware probability distribution and hierarchical loss for more accurate and consistent 3D human mesh reconstruction from incomplete video features.

Findings

Outperforms state-of-the-art methods on 3DPW dataset

Effectively handles occlusions and motion blur

Maintains motion consistency across frames

Abstract

Current 3D human motion reconstruction methods from monocular videos rely on features within the current reconstruction window, leading to distortion and deformations in the human structure under local occlusions or blurriness in video frames. To estimate realistic 3D human mesh sequences based on incomplete features, we propose Temporally-alignable Probability Guided Graph Topological Modeling for 3D Human Reconstruction (ProGraph). For missing parts recovery, we exploit the explicit topological-aware probability distribution across the entire motion sequence. To restore the complete human, Graph Topological Modeling (GTM) learns the underlying topological structure, focusing on the relationships inherent in the individual parts. Next, to generate blurred motion parts, Temporal-alignable Probability Distribution (TPDist) utilizes the GTM to predict features based on distribution. This interactive mechanism facilitates motion consistency, allowing the restoration of human parts. Furthermore, Hierarchical Human Loss (HHLoss) constrains the probability distribution errors of inter-frame features during topological structure variation. Our Method achieves superior results than other SOTA methods in addressing occlusions and blurriness on 3DPW.