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.

该论文提出了一种被称为 ROMP 的单阶段方法，通过学习每像素的形式同时预测一个身体中心热图和一个网格参数映射，从而达到多 3D 人体回归的效果。与现有的多阶段流水线方法相比，该方法免去了复杂的多阶段流程，更加稳健。