Improve paper and artifact metadata

README.md

@@ -3,14 +3,23 @@ license: mit
 library_name: pytorch
 tags:
   - co-speech-gesture-generation
+  - streaming-generation
+  - real-time-generation
   - motion-generation
   - smpl-x
   - streamtalk
+  - arxiv:2608.01643
 datasets:
   - H-Liu1997/BEAT2
 ---
 
-# StreamTalk retrained CFG checkpoints
+# StreamTalk official CFG checkpoints
+
+Official checkpoints for **StreamTalk: Streaming Co-Speech Gesture Generation with Key-Pose Anchoring** (ECCV 2026), a closed-loop method for real-time, long-horizon **co-speech gesture generation**.
+
+- [Paper](https://arxiv.org/abs/2608.01643) · [Hugging Face Paper page](https://huggingface.co/papers/2608.01643)
+- [Project page](https://xiangyuezhang.com/StreamTalk/) · [Code](https://github.com/Xiangyue-Zhang/StreamTalk)
+- [Generated inference data](https://huggingface.co/datasets/X-Zhang/StreamTalk-Inference-Data)
 
 These are hardware-agnostic FP32 generator state dictionaries for
 `DiffusionDITNetPartsFixedExpressions2PostNormInteraction2`. They were retrained
@@ -54,3 +63,18 @@ linked StreamTalk release and matching the published SHA256 values.
 End-to-end inference also requires WavLM Large, the bundled
 `SimpleSpeechModel`, SMPL-X neutral, and the BEAT2 retrieval database. Those
 runtime assets are independent of the generator checkpoint format.
+
+## Citation
+
+```bibtex
+@inproceedings{zhang2026streamtalk,
+  title={StreamTalk: Streaming Co-Speech Gesture Generation with Key-Pose Anchoring},
+  author={Zhang, Xiangyue and Li, Jianfang and Zhang, Jiaxu and Yang, Kaixing and Hoi, Steven},
+  booktitle={European Conference on Computer Vision},
+  year={2026},
+  eprint={2608.01643},
+  archivePrefix={arXiv},
+  primaryClass={cs.CV},
+  url={https://arxiv.org/abs/2608.01643}
+}
+```