Add model card for VideoMDM

Hi! I'm Niels from the Hugging Face community team. I noticed this repository was missing a model card, which is important for discoverability and usability.

This PR adds a comprehensive model card that:
- Includes metadata for the license and pipeline tag.
- Links the repository to the [original paper](https://huggingface.co/papers/2606.13364) and project page.
- Provides a guide for downloading the checkpoints and running the generation code as documented in the official repository.

README.md

@@ -0,0 +1,52 @@
+---
+license: mit
+pipeline_tag: other
+---
+
+# VideoMDM: Towards 3D Human Motion Generation From 2D Supervision
+
+VideoMDM is a diffusion-based framework that trains 3D human motion priors directly from 2D poses extracted from monocular videos, without requiring any 3D ground truth. This approach learns a coherent 3D motion manifold during training and produces high-quality motion generation across various datasets.
+
+- **Paper:** [VideoMDM: Towards 3D Human Motion Generation From 2D Supervision](https://huggingface.co/papers/2606.13364)
+- **Project Page:** [https://videomdm.github.io/](https://videomdm.github.io/)
+- **Repository:** [https://github.com/Amir-Mann/VideoMDM_release](https://github.com/Amir-Mann/VideoMDM_release)
+
+## Pretrained Checkpoints
+
+The following checkpoints are available in this repository:
+
+| Dataset | Lifter / teacher | Folder |
+|---|---|---|
+| HumanML3D | MVLift   | `HUMANML3D_VIDEOMDM_ON_MVLIFT/` |
+| Fit3D     | WHAM     | `FIT3D_VIDEOMDM_ON_WHAM/`       |
+| NBA       | ElePose  | `NBA_VIDEOMDM_ON_ELEPOSE/`      |
+
+## Usage
+
+This repository follows the structure and conventions of the [MDM (Human Motion Diffusion Model)](https://github.com/GuyTevet/motion-diffusion-model) repository.
+
+### Download Checkpoints
+You can download the checkpoints using the `huggingface_hub` CLI:
+
+```bash
+pip install -U huggingface_hub
+hf download AmirMann/VideoMDM --local-dir ./save
+```
+
+### Generation
+To generate motion using a downloaded checkpoint, run the following command (pointing to the specific `.pt` file). The matching `args.json` in the same folder will be loaded automatically:
+
+```bash
+python -m sample.generate --model_path ./save/HUMANML3D_VIDEOMDM_ON_MVLIFT/model000600091.pt
+```
+
+## Citation
+
+```bibtex
+@article{mann2024videomdm,
+  title={VideoMDM: Towards 3D Human Motion Generation From 2D Supervision},
+  author={Mann, Amir and Harari, Gal Michael and Keidar, Merav and Litany, Or},
+  journal={arXiv preprint arXiv:2606.13364},
+  year={2024}
+}
+```