  Reconstructing multi-human body mesh from a single monocular image is an
important but challenging computer vision problem. In addition to the
individual body mesh models, we need to estimate relative 3D positions among
subjects to generate a coherent representation. In this work, through a single
graph neural network, named MUG (Multi-hUman Graph network), we construct
coherent multi-human meshes using only multi-human 2D pose as input. Compared
with existing methods, which adopt a detection-style pipeline (i.e., extracting
image features and then locating human instances and recovering body meshes
from that) and suffer from the significant domain gap between lab-collected
training datasets and in-the-wild testing datasets, our method benefits from
the 2D pose which has a relatively consistent geometric property across
datasets. Our method works like the following: First, to model the multi-human
environment, it processes multi-human 2D poses and builds a novel heterogeneous
graph, where nodes from different people and within one person are connected to
capture inter-human interactions and draw the body geometry (i.e., skeleton and
mesh structure). Second, it employs a dual-branch graph neural network
structure -- one for predicting inter-human depth relation and the other one
for predicting root-joint-relative mesh coordinates. Finally, the entire
multi-human 3D meshes are constructed by combining the output from both
branches. Extensive experiments demonstrate that MUG outperforms previous
multi-human mesh estimation methods on standard 3D human benchmarks --
Panoptic, MuPoTS-3D and 3DPW.
