LASOR: Learning Accurate 3D Human Pose and Shape Via Synthetic Occlusion-Aware Data and Neural Mesh Rendering

• URL: http://arxiv.org/abs/2108.00351v1
• Date: Sun, 1 Aug 2021 02:09:16 GMT
• Title: LASOR: Learning Accurate 3D Human Pose and Shape Via Synthetic Occlusion-Aware Data and Neural Mesh Rendering
• Authors: Kaibing Yang, Renshu Gu, Masahiro Toyoura and Gang Xu
• Abstract summary: We propose a framework that synthesizes silhouette and 2D keypoints data and directly regress to the SMPL pose and shape parameters. A neural 3D mesh is exploited to enable silhouette supervision on the fly, which contributes to great improvements in shape estimation. We are among state-of-the-art on the 3DPW dataset in terms of pose accuracy and evidently outperform the rank-1 method in terms of shape accuracy.
• Score: 3.007707487678111
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A key challenge in the task of human pose and shape estimation is occlusion, including self-occlusions, object-human occlusions, and inter-person occlusions. The lack of diverse and accurate pose and shape training data becomes a major bottleneck, especially for scenes with occlusions in the wild. In this paper, we focus on the estimation of human pose and shape in the case of inter-person occlusions, while also handling object-human occlusions and self-occlusion. We propose a framework that synthesizes occlusion-aware silhouette and 2D keypoints data and directly regress to the SMPL pose and shape parameters. A neural 3D mesh renderer is exploited to enable silhouette supervision on the fly, which contributes to great improvements in shape estimation. In addition, keypoints-and-silhouette-driven training data in panoramic viewpoints are synthesized to compensate for the lack of viewpoint diversity in any existing dataset. Experimental results show that we are among state-of-the-art on the 3DPW dataset in terms of pose accuracy and evidently outperform the rank-1 method in terms of shape accuracy. Top performance is also achieved on SSP-3D in terms of shape prediction accuracy.

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