  We consider the challenging multi-person 3D body mesh estimation task in this
work. Existing methods are mostly two-stage based--one stage for person
localization and the other stage for individual body mesh estimation, leading
to redundant pipelines with high computation cost and degraded performance for
complex scenes (e.g., occluded person instances). In this work, we present a
single-stage model, Body Meshes as Points (BMP), to simplify the pipeline and
lift both efficiency and performance. In particular, BMP adopts a new method
that represents multiple person instances as points in the spatial-depth space
where each point is associated with one body mesh. Hinging on such
representations, BMP can directly predict body meshes for multiple persons in a
single stage by concurrently localizing person instance points and estimating
the corresponding body meshes. To better reason about depth ordering of all the
persons within the same scene, BMP designs a simple yet effective
inter-instance ordinal depth loss to obtain depth-coherent body mesh
estimation. BMP also introduces a novel keypoint-aware augmentation to enhance
model robustness to occluded person instances. Comprehensive experiments on
benchmarks Panoptic, MuPoTS-3D and 3DPW clearly demonstrate the
state-of-the-art efficiency of BMP for multi-person body mesh estimation,
together with outstanding accuracy. Code can be found at:
https://github.com/jfzhang95/BMP.
