Papers › MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose
MUG: Multi-human Graph Network for 3D Mesh Reconstruction from 2D Pose
Chenyan Wu, Yandong Li, Xianfeng Tang, James Wang
Reconstructing multi-human body mesh from a single monocular image is an important but challenging computer vision problem. In addition to the individual body mesh models, we need to estimate relative 3D positions among subjects to generate a coherent representation. In this work, through a single graph neural network, named MUG (Multi-hUman Graph network), we construct coherent multi-human meshes using only multi-human 2D pose as input. Compared with existing methods, which adopt a detection-style pipeline (i.e., extracting image features and then locating human instances and recovering body meshes from that) and suffer from the significant domain gap between lab-collected training datasets and in-the-wild testing datasets, our method benefits from the 2D pose which has a relatively consistent geometric property across datasets. Our method works like the following: First, to model the multi-human environment, it processes multi-human 2D poses and builds a novel heterogeneous graph, where nodes from different people and within one person are connected to capture inter-human interactions and draw the body geometry (i.e., skeleton and mesh structure). Second, it employs a dual-branch graph neural network structure -- one for predicting inter-human depth relation and the other one for predicting root-joint-relative mesh coordinates. Finally, the entire multi-human 3D meshes are constructed by combining the output from both branches. Extensive experiments demonstrate that MUG outperforms previous multi-human mesh estimation methods on standard 3D human benchmarks -- Panoptic, MuPoTS-3D and 3DPW.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Human Pose Estimation | 3DPW | MUG | MPJPE | 87 | #57 of 119 | Archive leaderboard | report |
| 3D Human Pose Estimation | 3DPW | MUG | MPVPE | 106.2 | #57 of 119 | Archive leaderboard | report |
| 3D Human Pose Estimation | 3DPW | MUG | PA-MPJPE | 60.5 | #57 of 119 | Archive leaderboard | report |
| 3D Human Pose Estimation | Panoptic | MUG | Average MPJPE (mm) | 127.8 | #9 of 9 | Archive leaderboard | report |
| 3D Multi-Person Pose Estimation | MuPoTS-3D | MUG | 3DPCK | 76.27 | #5 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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