Name
Abstract | ||
|---|---|---|
We consider the problem of recovering a single person's 3D human mesh from in-the-wild crowded scenes. While much progress has been in 3D human mesh estimation, existing methods struggle when test input has crowded scenes. The first reason for the failure is a domain gap between training and testing data. A motion capture dataset, which provides accurate 3D labels for training, lacks crowd data and impedes a network from learning crowded scene-robust image features of a target person. The second reason is a feature processing that spatially averages the feature map of a localized bounding box containing multiple people. Averaging the whole feature map makes a target person's feature indistinguishable from others. We present 3DCrowdNet that firstly explicitly targets in-the-wild crowded scenes and estimates a robust 3D human mesh by addressing the above issues. First, we leverage 2D human pose estimation that does not require a motion capture dataset with 3D labels for training and does not suffer from the domain gap. Second, we propose a joint-based regressor that distinguishes a target person's feature from others. Our joint-based regressor preserves the spatial activation of a target by sampling features from the target's joint locations and regresses human model parameters. As a result, 3DCrowdNet learns target-focused features and effectively excludes the irrelevant features of nearby persons. We conduct experiments on various benchmarks and prove the robustness of 3D CrowdNet to the in-the-wild crowded scenes both quantitatively and qualitatively. Codes are available here 1 1 https://github.com/hongsukchoi/3DCrowdNet_RELEASE. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPR52688.2022.00153 | IEEE Conference on Computer Vision and Pattern Recognition |
Keywords | DocType | Volume |
3D from single images, Face and gestures | Conference | 2022 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hongsuk Choi | 1 | 0 | 0.34 |
| Gyeongsik Moon | 2 | 0 | 1.35 |
| JoonKyu Park | 3 | 0 | 0.34 |
| Kyoung Mu Lee | 4 | 3228 | 153.84 |