  Existing multi-person human reconstruction approaches mainly focus on
recovering accurate poses or avoiding penetration, but overlook the modeling of
close interactions. In this work, we tackle the task of reconstructing closely
interactive humans from a monocular video. The main challenge of this task
comes from insufficient visual information caused by depth ambiguity and severe
inter-person occlusion. In view of this, we propose to leverage knowledge from
proxemic behavior and physics to compensate the lack of visual information.
This is based on the observation that human interaction has specific patterns
following the social proxemics. Specifically, we first design a latent
representation based on Vector Quantised-Variational AutoEncoder (VQ-VAE) to
model human interaction. A proxemics and physics guided diffusion model is then
introduced to denoise the initial distribution. We design the diffusion model
as dual branch with each branch representing one individual such that the
interaction can be modeled via cross attention. With the learned priors of
VQ-VAE and physical constraint as the additional information, our proposed
approach is capable of estimating accurate poses that are also proxemics and
physics plausible. Experimental results on Hi4D, 3DPW, and CHI3D demonstrate
that our method outperforms existing approaches. The code is available at
\url{https://github.com/boycehbz/HumanInteraction}.
