Monocular, One-Stage, Regression of Multiple 3D People

2021

Conference Paper

ps

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.

@inproceedings{ROMP:ICCV:2021,
  title = {Monocular, One-Stage, Regression of Multiple {3D} People},
  author = {Sun, Yu and Bao, Qian and Liu, Wu and Fu, Yili and Black, Michael J. and Mei, Tao},
  booktitle = {Proc. International Conference on Computer Vision (ICCV)},
  pages = {11159--11168},
  publisher = {IEEE},
  address = {Piscataway, NJ},
  month = oct,
  year = {2021},
  doi = {10.1109/ICCV48922.2021.01099},
  month_numeric = {10}
}