Update README.md

README.md

@@ -1,3 +1,45 @@
 ---
 license: mit
+library_name: pytorch
+tags:
+- computer-vision
+- 3d-reconstruction
+- gaussian-splatting
+- head-avatar
+- animation
+- cvpr-2026
 ---
+
+# UIKA
+
+This repository hosts the released model weights and auxiliary assets for **UIKA: Fast Universal Head Avatar from Pose-Free Images**.
+
+UIKA is a feed-forward model for creating animatable 3D Gaussian head avatars from pose-free inputs. It supports a single reference image, multiple images of the same identity, or smartphone-style video captures. Use these files together with the official PyTorch code:
+
+- Code: https://github.com/ant-research/UIKA
+- Paper: https://arxiv.org/abs/2601.07603
+- Project page: https://zijian-wu.github.io/uika-page/
+
+## Files
+
+- `uika.safetensors` - main UIKA checkpoint. The official inference config expects it at `model_zoo/uika/uika.safetensors`.
+- `fuvt_15k.safetensors` - FUVT UV-estimation module checkpoint used by UIKA.
+- `human_parametric_models.tar` - human parametric model assets required by the pipeline. Third-party assets may have separate license terms.
+- `ref_motion_example.tar` - example reference/motion assets for testing inference.
+
+## License
+
+The UIKA-authored code and the released UIKA/FUVT checkpoints are released under the MIT License. Third-party model weights, datasets, and human parametric model assets remain subject to their own licenses and access terms. Review the official repository's third-party notices before redistribution or commercial use.
+
+## Citation
+
+If you use UIKA in your work, please cite:
+
+```bibtex
+@inproceedings{wu2026uika,
+    title     = {UIKA: Fast Universal Head Avatar from Pose-Free Images},
+    author    = {Wu, Zijian and Zhou, Boyao and Hu, Liangxiao and Liu, Hongyu and Sun, Yuan and Wang, Xuan and Cao, Xun and Shen, Yujun and Zhu, Hao},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+    year      = {2026}
+}
+```