Add model card and evaluation checkpoint

README.md

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+---
+tags:
+- music-to-dance
+- dance-generation
+- 3d-human-motion
+- motion-generation
+- meanflow
+- bimamba
+---
+
+# FlowerDance
+
+## MeanFlow for Efficient and Refined 3D Dance Generation
+
+**Accepted to ECCV 2026**
+
+[Paper](https://arxiv.org/abs/2511.21029) |
+[Project Page](https://sun-happy-ykx.github.io/FlowerDance/) |
+[GitHub](https://github.com/XulongT/FlowerDance)
+
+![FlowerDance teaser](https://raw.githubusercontent.com/XulongT/FlowerDance/main/flowerteaser.png)
+
+FlowerDance generates physically plausible and artistically expressive 3D dance
+with efficient inference and memory usage. It combines MeanFlow with Physical
+Consistency Constraints for high-quality few-step sampling and uses a
+lightweight, non-autoregressive BiMamba backbone with Channel-Level Fusion.
+
+## Checkpoint
+
+This repository provides the evaluation checkpoint released with FlowerDance:
+
+- `train-3700.pt`
+
+Download it with:
+
+```python
+from huggingface_hub import hf_hub_download
+
+checkpoint_path = hf_hub_download(
+    repo_id="xlt99/FlowerDance",
+    filename="train-3700.pt",
+)
+print(checkpoint_path)
+```
+
+To use the checkpoint with the
+[official code](https://github.com/XulongT/FlowerDance), place it at:
+
+```text
+runs/train/uniform2/weights/train-3700.pt
+```
+
+Then follow the evaluation instructions in the GitHub README.
+
+## Citation
+
+```bibtex
+@inproceedings{yang2026flowerdance,
+  title={FlowerDance: MeanFlow for Efficient and Refined 3D Dance Generation},
+  author={Kaixing Yang and Xulong Tang and Ziqiao Peng and Xiangyue Zhang and Puwei Wang and Jun He and Hongyan Liu},
+  booktitle={European Conference on Computer Vision (ECCV)},
+  year={2026}
+}
+```
+
+## License
+
+The release does not currently declare a license. Please contact the authors
+for permitted uses.

train-3700.pt

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