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Detail
Conference Paper
Instance-aware Contrastive Learning for Occluded Human Mesh Reconstruction
- Authors
- Mi-Gyeong Gwon, Gi-Mun Um, Won-Sik Cheong, Wonjun Kim
- Issue Date
- 2024-06
- Citation
- Conference on Computer Vision and Pattern Recognition (CVPR) 2024, pp.10553-10562
- Language
- English
- Type
- Conference Paper
- Abstract
- A simple yet effective method for occlusion-robust 3D human mesh reconstruction from a single image is presented in this paper. Although many recent studies have shown the remarkable improvement in human mesh reconstruction, it is still difficult to generate accurate meshes when person-to-person occlusion occurs due to the ambigu-ity of who a body part belongs to. To address this problem, we propose an instance-aware contrastive learning scheme. Specifically, joint features belonging to the target human are trained to be proximate with the center feature (i.e., feature extracted from the body center position). On the other hand, center features of different human instances are forced to be far apart so that joint features of each person can be clearly distinguished from others. By interpreting the joint possession based on such contrastive learning scheme, the proposed method easily understands the spatial occupancy of body parts for each person in a given image, thus can reconstruct reliable human meshes even with severely overlapped cases between multiple persons. Ex-perimental results on benchmark datasets demonstrate the robustness of the proposed method compared to previous approaches under person-to-person occlusions. The code and model are publicly available at: https://github.com/DCVL-3D/InstanceHMR-release.
- KSP Keywords
- Benchmark datasets, Body parts, Joint features, Mesh reconstruction, Single image, feature extracted, the body