Learning 3D Human Pose and Shape Estimation Using Uncertainty-Aware Body Part Segmentation

While exploiting body segmentations for supervision, existing 3D human pose and shape estimation methods are plagued by mismatches between clothed body segmentations and skinned SMPL model reprojections. Moreover, noisy pixels introduced by inaccurate segmentation annotations also prevent the model from improving the reconstruction performance further. To address these problems, we propose a novel generalizable framework called Uncertainty-aware body Part Segmentation (UPS), which penalizes different body parts with data uncertainty estimation. Specifically, we use sample-specific segmentations to supervise skinned and clothed body parts separately for realistic human mesh recovery. Furthermore, we leverage data uncertainty to improve the model capacity via learning from representative pixels and resisting noisy ones. Our extensive qualitative and quantitative experiments show that UPS achieves competitive reconstruction results on standard benchmarks.