  Estimating 3D poses and shapes in the form of meshes from monocular RGB
images is challenging. Obviously, it is more difficult than estimating 3D poses
only in the form of skeletons or heatmaps. When interacting persons are
involved, the 3D mesh reconstruction becomes more challenging due to the
ambiguity introduced by person-to-person occlusions. To tackle the challenges,
we propose a coarse-to-fine pipeline that benefits from 1) inverse kinematics
from the occlusion-robust 3D skeleton estimation and 2) Transformer-based
relation-aware refinement techniques. In our pipeline, we first obtain
occlusion-robust 3D skeletons for multiple persons from an RGB image. Then, we
apply inverse kinematics to convert the estimated skeletons to deformable 3D
mesh parameters. Finally, we apply the Transformer-based mesh refinement that
refines the obtained mesh parameters considering intra- and inter-person
relations of 3D meshes. Via extensive experiments, we demonstrate the
effectiveness of our method, outperforming state-of-the-arts on 3DPW, MuPoTS
and AGORA datasets.
