ProGraph: Temporally-alignable Probability Guided Graph Topological Modeling for 3D Human Reconstruction
This addresses the issue of distortion and deformations in 3D human reconstruction for applications like animation or virtual reality, representing a strong specific gain in handling occlusions and blurriness.
The paper tackles the problem of 3D human motion reconstruction from monocular videos under occlusions or blurriness, proposing ProGraph to estimate realistic 3D human mesh sequences and achieving superior results compared to other state-of-the-art methods on the 3DPW dataset.
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.