RAGT: Learning Robust Features for Occluded Human Pose and Shape Estimation with Attention-Guided Transformer

3D human pose and shape estimation from monocular images is a fundamental task in computer vision, but it is highly ill-posed and challenging due to occlusion. Occlusion can be caused by other objects that block parts of the body from being visible in the image. When an occlusion occurs, the image features become incomplete and ambiguous, leading to inaccurate or even wrong predictions. In this paper, we propose a novel method, named RAGT, that can handle occlusion robustly and recover the complete 3D pose and shape of humans. Our study focuses on achieving robust feature representation for human pose and shape estimation in the presence of occlusion. To this end, we introduce a dual-branch architecture that learns incorporation weights from visible parts to occluded parts and suppression weights to inhibit the integration of background features. To further improve the quality of visible and occluded maps, we leverage pseudo ground-truth maps generated by DensePose for pixel-level supervision. Additionally, we propose a novel transformer-based module called COAT (Contextual Occlusion-Aware Transformer) to effectively incorporate visible features into occluded regions. The COAT module is guided by an Occlusion-Guided Attention Loss (OGAL). OGAL is designed to explicitly encourage the COAT module to fuse more important and relevant features that are semantically and spatially closer to the occluded regions. We conduct experiments on various benchmarks and prove the robustness of RAGT to the different kinds of occluded scenes both quantitatively and qualitatively.