| --- |
| license: mit |
| tags: |
| - arxiv:2605.13854 |
| --- |
| |
| # CoMHR |
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| **Contrastive Multi-Modal Hypergraph Reasoning for 3D Crowd Mesh Recovery** |
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| Official model weights for our **ICME 2026 Oral** paper. |
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| **Paper:** https://arxiv.org/abs/2605.13854 |
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| **Code:** https://github.com/SunMH-try/CoMHR |
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| ## Pretrained Weights |
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| Download `CoHMR_weights.zip` from this repository. |
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| The pretrained model is also available on [Baidu Netdisk](https://pan.baidu.com/s/1ar3jVceyKqUiLuGoqGX1Ww?pwd=k3qd) (code: `k3qd`). |
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| ## Ablation Checkpoints |
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| Additional checkpoints from our ablation experiments are available in the [abl_ckpt](https://huggingface.co/Sunm08/CoMHR/tree/main/abl_ckpt) directory. |
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| | Checkpoint | Modalities | Panoptic MPJPE ↓ | |
| |---|---|---| |
| | `rgb.pkl` | RGB | 110.41 mm | |
| | `depth.pkl` | Depth | 137.40 mm | |
| | `pose.pkl` | Pose | 184.36 mm | |
| | `multimodal_no_contra.pkl` | RGB + Depth + Pose | 105.26 mm | |
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| The single-modal checkpoints serve as baselines, while `multimodal_no_contra.pkl` represents the multimodal model with pelvis-depth refinement but without contrastive learning. |
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| The full CoMHR method achieves **104.18 mm MPJPE** on Panoptic. |
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| ## Usage |
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| For model architecture, configuration, dependencies, and inference instructions, please refer to the [official GitHub repository](https://github.com/SunMH-try/CoMHR). |
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| ## Citation |
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| If you find our work useful, please consider citing: |
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| ```bibtex |
| @inproceedings{sun2026contrastive, |
| title = {Contrastive Multi-Modal Hypergraph Reasoning for 3D Crowd Mesh Recovery}, |
| author = {Sun, Minghao and Xu, Chongyang and Xie, Yitao and Huang, Buzhen and Li, Kun}, |
| booktitle = {Proceedings of the IEEE International Conference on Multimedia and Expo (ICME)}, |
| year = {2026} |
| } |
