  This paper focuses on the regression of multiple 3D people from a single RGB
image. Existing approaches predominantly follow a multi-stage pipeline that
first detects people in bounding boxes and then independently regresses their
3D body meshes. In contrast, we propose to Regress all meshes in a One-stage
fashion for Multiple 3D People (termed ROMP). The approach is conceptually
simple, bounding box-free, and able to learn a per-pixel representation in an
end-to-end manner. Our method simultaneously predicts a Body Center heatmap and
a Mesh Parameter map, which can jointly describe the 3D body mesh on the pixel
level. Through a body-center-guided sampling process, the body mesh parameters
of all people in the image are easily extracted from the Mesh Parameter map.
Equipped with such a fine-grained representation, our one-stage framework is
free of the complex multi-stage process and more robust to occlusion. Compared
with state-of-the-art methods, ROMP achieves superior performance on the
challenging multi-person benchmarks, including 3DPW and CMU Panoptic.
Experiments on crowded/occluded datasets demonstrate the robustness under
various types of occlusion. The released code is the first real-time
implementation of monocular multi-person 3D mesh regression.
