Improve model card: add pipeline tag, paper link, and structured information

This PR improves the model card for LiveTalk. Specifically, it:
- Adds the `image-to-video` pipeline tag to the metadata to improve discoverability.
- Links the original paper and the official GitHub repository.
- Includes key highlights of the model such as its real-time performance and multimodal capabilities.
- Adds a sample usage section with the inference command provided in the GitHub documentation.
- Adds the BibTeX citation for researchers.

README.md

@@ -2,9 +2,9 @@
 license: apache-2.0
 tags:
 - agent
+pipeline_tag: image-to-video
 ---
 
-
 # LiveTalk: Real-Time Multimodal Interactive Video Diffusion via Improved On-Policy Distillation
 
 <div align="center">
@@ -21,12 +21,54 @@ tags:
   </a>
 </p>
 
-This is the model for https://github.com/GAIR-NLP/LiveTalk
+This is the official model repository for [LiveTalk](https://github.com/GAIR-NLP/LiveTalk).
 
-**LiveTalk** enables real-time multimodal interactive avatar video generation through an improved on-policy distillation approach.
-By distilling bidirectional diffusion models into causal, few-step autoregressive models, LiveTalk achieves over **20× speedup**,
-enabling seamless real-time interactive experience.
+**LiveTalk** enables real-time multimodal interactive avatar video generation through an improved on-policy distillation approach. By distilling bidirectional diffusion models into causal, few-step autoregressive models, LiveTalk achieves over **20× speedup**, enabling a seamless real-time interactive experience.
 
 <p align="center">
   <img src="assets/overview.png" alt="LiveTalk System Overview" width="100%">
-</p>
+</p>
+
+## ⭐ Highlights
+
+- **Real-Time Generation**: Achieves 24.82 FPS throughput with 0.33s first-frame latency.
+- **Multimodal Conditioning**: Supports text, image, and audio inputs for flexible avatar control.
+- **Efficient Inference**: Reduces inference time from ~83s to real-time through 4-step diffusion distillation.
+- **Multi-Turn Coherence**: Demonstrates competitive performance against state-of-the-art models in multi-round interaction benchmarks.
+- **End-to-End System**: Provides integration with audio language models for conversational AI applications.
+
+## 🚀 Get started
+
+### Installation
+
+For detailed setup instructions, including environment configuration and dependency installation, please refer to the [official GitHub repository](https://github.com/GAIR-NLP/LiveTalk).
+
+### Inference
+
+Once the environment and checkpoints are prepared, you can execute the inference script:
+
+```bash
+bash ./scripts/inference.sh
+```
+
+**Input Requirements:**
+- **Image**: Reference image of the person (JPG/PNG format).
+- **Audio**: Speech audio file (WAV format, 16kHz sample rate recommended).
+- **Text Prompt**: Description of the desired video characteristics.
+
+## 🔍 Method Overview
+
+LiveTalk addresses challenges in distilling multimodal video diffusion models by using an improved on-policy distillation recipe. It introduces curated multimodal conditions, converged ODE initialization, and aggressive optimization to eliminate training instability (like flickering or black frames) while delivering high-quality, lip-synced results.
+
+## 📚 Citation
+
+If you find this work useful for your research, please cite:
+
+```bibtex
+@article{livetalk2025,
+  title={LiveTalk: Real-Time Multimodal Interactive Video Diffusion via Improved On-Policy Distillation},
+  author={Chern, Ethan and Hu, Zhulin and Tang, Bohao and Su, Jiadi and Chern, Steffi and Deng, Zhijie and Liu, Pengfei},
+  journal={arXiv preprint arXiv:2512.23576},
+  year={2025}
+}
+```