Update license, add pipeline tag and improve model card

Hi! This is Niels from the community science team at Hugging Face.

This PR improves the model card for SCAIL by:
- Adding the `image-to-video` pipeline tag for better discoverability.
- Updating the license to `apache-2.0` to match the official GitHub repository.
- Adding descriptive tags and linking author Hugging Face profiles.
- Linking the paper to the Hugging Face papers page in the markdown section.

README.md

@@ -1,12 +1,18 @@
 ---
-license: mit
+license: apache-2.0
+pipeline_tag: image-to-video
+tags:
+- character-animation
+- 3d-pose
+- motion-transfer
 ---
+
 # SCAIL: Towards Studio-Grade Character Animation via In-Context Learning of 3D-Consistent Pose Representations
 
 
 <div align="center">
-  <a href="https://arxiv.org/abs/2512.05905">
-    <img src="https://img.shields.io/badge/📖%20arXiv-2512.05905-red">
+  <a href="https://huggingface.co/papers/2512.05905">
+    <img src="https://img.shields.io/badge/📖%20Paper-2512.05905-red">
   </a>
   <a href="https://teal024.github.io/SCAIL/">
     <img src="https://img.shields.io/badge/🌐%20Project%20Page-green">
@@ -17,14 +23,20 @@ license: mit
 </div>
 
 
-This repository contains the model weights for **SCAIL (Studio-Grade Character Animation via In-Context Learning)**, a framework that enables high-fidelity character animation under diverse and challenging conditions, including large motion variations, stylized characters, and multi-character interactions.
+**SCAIL** (Studio-grade Character Animation via In-context Learning) is a framework designed to achieve high-fidelity character animation that meets studio standards. It addresses challenges in preserving structural fidelity and temporal consistency during motion transfer, especially in complex scenarios involving large motions and multi-character interactions.
 
-## 🔎 Project Page
-Check our model architecture design, our video demo, as well as more comparisons against other baselines at [this link](https://teal024.github.io/SCAIL/), more creative examples will be added to the gallery soon.
+Key features include:
+- **3D-Consistent Pose Representations**: Provides a robust and flexible motion signal while preventing identity leakage.
+- **Full-Context Pose Injection**: Enables effective spatio-temporal reasoning over entire motion sequences within a diffusion-transformer.
+- **Studio-Grade Quality**: Trained on a curated data pipeline to ensure diversity and quality.
 
+This repository contains the model weights for the **SCAIL-Preview (14B)** model.
+
+## 🔎 Project Page
+Check the model architecture design, video demos, and comparisons against other baselines at the [official project page](https://teal024.github.io/SCAIL/).
 
 ## 📋 Note
-This repository only contains the model weights for the SCAIL model, for model inference and our plan, please refer to the [official repository](https://github.com/teal024/SCAIL-Official). For pose extraction, please refer to the [SCAIL-Pose](https://github.com/teal024/SCAIL-Pose).
+This repository contains the model weights for the SCAIL model. For model inference, environment setup, and the roadmap, please refer to the [official repository](https://github.com/teal024/SCAIL-Official). For pose extraction tools, refer to [SCAIL-Pose](https://github.com/teal024/SCAIL-Pose).
 
 ## 📄 Citation
 
@@ -33,8 +45,11 @@ If you find this work useful in your research, please cite:
 ```bibtex
 @article{yan2025scail,
   title={SCAIL: Towards Studio-Grade Character Animation via In-Context Learning of 3D-Consistent Pose Representations},
-  author={Yan, Wenhao and Ye, Sheng and Yang, Zhuoyi and Teng, Jiayan and Dong, ZhenHui and Wen, Kairui and Gu, Xiaotao and Liu, Yong-Jin and Tang, Jie},
+  author={Yan, Wenhao and Ye, Sheng English and Yang, Zhuoyi and Teng, Jiayan and Dong, ZhenHui and Wen, Kairui and Gu, Xiaotao and Liu, Yong-Jin and Tang, Jie},
   journal={arXiv preprint arXiv:2512.05905},
   year={2025}
 }
 ```
+
+## 👥 Authors
+[Wenhao Yan](https://huggingface.co/wenhaoyan77), Sheng Ye, Zhuoyi Yang, [Jiayan Teng](https://huggingface.co/tengjiayan), ZhenHui Dong, [Kairui Wen](https://huggingface.co/SKearbvaanl), [Xiaotao Gu](https://huggingface.co/xgeric), Yong-Jin Liu, [Jie Tang](https://huggingface.co/jerytang).