Monocular, One-stage, Regression of Multiple 3D People
By Yu Sun, Qian Bao, Wu Liu, Yili Fu, Michael J. Black, Tao Mei. IEEE International Conference on Computer Vision, 2020
346 citations recorded by the source corpus.
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 code, released at https://github.com/Arthur151/ROMP, is the first real-time implementation of monocular multi-person 3D mesh regression.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/232e6c63c1bae395520ba6c13668e6cca98b855d
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
In Computer Vision and Pattern Recognition
- Oncel Tuzel
- Kwang Moo Yi
- Anurag Ranjan
- Golnoosh Samei
- Wei Jiang
- Luc Van Gool
- Ming-Hsuan Yang
- Xiaogang Wang
Citations in the corpus
- CLIFF: Carrying Location Information in Full Frames into Human Pose and Shape Estimation (2022, neutral): “Like most tasks in computer vision, there are two approaches to do this: top-down [16,23,20,21] and bottom-up [49,56].”
- NeuMan: Neural Human Radiance Field from a Single Video (2022, positive): “We refine the SMPL estimates from ROMP [42] to better serve the training.”