  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 novel 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
the state-of-the-art on the 3DPW and 3DPW-Crowd datasets in terms of pose
estimation accuracy. The proposed method evidently outperforms Mesh
Transformer, 3DCrowdNet and ROMP in terms of shape estimation. Top performance
is also achieved on SSP-3D in terms of shape prediction accuracy. Demo and code
will be available at https://igame-lab.github.io/LASOR/.
