This paper introduces a novel pipeline to reconstruct the geometry of
interacting multi-person in clothing on a globally coherent scene space from a
single image. The main challenge arises from the occlusion: a part of a human
body is not visible from a single view due to the occlusion by others or the
self, which introduces missing geometry and physical implausibility (e.g.,
penetration). We overcome this challenge by utilizing two human priors for
complete 3D geometry and surface contacts. For the geometry prior, an encoder
learns to regress the image of a person with missing body parts to the latent
vectors; a decoder decodes these vectors to produce 3D features of the
associated geometry; and an implicit network combines these features with a
surface normal map to reconstruct a complete and detailed 3D humans. For the
contact prior, we develop an image-space contact detector that outputs a
probability distribution of surface contacts between people in 3D. We use these
priors to globally refine the body poses, enabling the penetration-free and
accurate reconstruction of interacting multi-person in clothing on the scene
space. The results demonstrate that our method is complete, globally coherent,
and physically plausible compared to existing methods.

本文介绍了一种重建单张图像中多人穿着服装的相互作用几何结构的新型流程。通过利用两个人体先验来解决遮挡带来的挑战，使用几何先验和表面接触先验，在场景空间中全局细化人体姿态，实现无穿透的准确重建。与现有方法相比，结果表明我们的方法具有完备性、全局一致性和物理可行性。