Reconstructing sharp and animatable 3D human avatars from motion-blurred videos remains challenging under partial occlusion, where unreliable SMPL estimates can propagate pose errors into Gaussian deformation. To address this problem, we propose OAM-GA, an occlusion-robust framework for motion-aware Gaussian avatar reconstruction. OAM-GA estimates joint-wise reliability and restores missing motion using visible temporal context. Specifically, a Bidirectional Wavelet Masked Motion Completion module decomposes motion features into low- and high-frequency components and reconstructs contiguous occluded intervals from observations before and after the masked region. Clean–occluded contrastive consistency learning further encourages occlusion-invariant motion representations, while reliability-guided residual refinement selectively corrects uncertain joints and preserves reliable ones. Experiments on BlurZJU and BS-Human show that OAM-GA improves overall reconstruction robustness and perceptual quality under diverse occlusion patterns while maintaining the rendering fidelity of the original baseline.
@unpublished{wang2026oamga,title={{OAM-GA}: Reliability-Guided Motion Completion for Occlusion-RobustGaussian Avatars},author={Wang, Xiang and Yan, Xu and Pei, Letian and Yan, Weiping},year={2026},note={Submitted to {DAI} 2026; under review, decision pending, and not yet accepted or published.},url={https://openreview.net/forum?id=UV2UJ4VHf7},}
@article{yan2023pm25,title={Effect of {PM2.5} air pollution on the incidence of respiratory diseases: A {Python}-based data analysis},author={Yan, Weiping},journal={Theoretical and Natural Science},volume={8},number={1},pages={70--75},year={2023},month=nov,publisher={EWA Publishing},doi={10.54254/2753-8818/8/20240361},url={https://tns.ewapub.com/article/view/6279},}