Update README.md

README.md

@@ -1,3 +1,74 @@
----
-license: cc-by-nc-4.0
----
+````markdown
+---
+license: cc-by-nc-4.0
+---
+
+# ResponseNet
+
+**ResponseNet** is a large-scale dyadic video dataset designed for **Online Multimodal Conversational Response Generation (OMCRG)**. It fills the gap left by existing datasets by providing high-resolution, split-screen recordings of both speaker and listener, separate audio channels, and word‑level textual annotations for both participants.
+
+## Paper
+
+If you use this dataset, please cite:
+
+> **ResponseNet: A High‑Resolution Dyadic Video Dataset for Online Multimodal Conversational Response Generation**  
+> *Authors: Luo, Cheng and Wang, Jianghui and Li, Bing and Song, Siyang and Ghanem, Bernard*  
+
+
+## Features
+
+- **696** temporally synchronized dyadic video pairs (over **14 hours** total).
+- **High-resolution** (1024×1024) frontal‑face streams for both speaker and listener.
+- **Separate audio channels** for fine‑grained verbal and nonverbal analysis.
+- **Word‑level textual annotations** for both participants.
+- **Longer clips** (average **73.39 s**) than REACT2024 (30 s) and Vico (9 s), capturing richer conversational exchanges.
+- **Diverse topics**: professional discussions, emotionally driven interactions, educational settings, interdisciplinary expert talks.
+- **Balanced splits**: training, validation, and test sets with equal distributions of topics, speaker identities, and recording conditions.
+
+## Data Fields
+
+Each example in the dataset is a dictionary with the following fields:
+
+- `video/speaker`: Path to the speaker’s video stream (1024×1024, frontal view).
+- `video/listener`: Path to the listener’s video stream (1024×1024, frontal view).
+- `audio_speaker`: Path to the speaker’s separated audio channel.
+- `audio/listener`: Path to the listener’s separated audio channel.
+- `transcript/speaker`: Word‑level transcription for the speaker (timestamps included).
+- `transcript/listener`: Word‑level transcription for the listener (timestamps included).
+- `vector/speaker`: Path to the speaker’s facial attributes.
+- `vector/listener`: Path to the listener’s facial attributes.
+
+## Dataset Splits
+
+We follow a standard **6:2:2** split ratio, ensuring balanced distributions of topics, identities, and recording conditions:
+
+| Split      | # Video Pairs | Proportion (%) |
+|------------|---------------|----------------|
+| **Train**  | 417           | 59.9           |
+| **Valid**  | 139           | 20.0           |
+| **Test**   | 140           | 20.1           |
+| **Total**  | 696           | 100.0          |
+
+
+
+## Visualization
+
+You can visualize word‑cloud statistics, clip‑duration distributions, and topic breakdowns using standard Python plotting tools.
+
+## Citation
+
+```bibtex
+@article{luo2025omniresponse,
+  title={OmniResponse: Online Multimodal Conversational Response Generation in Dyadic Interactions},
+  author={Luo, Cheng and Wang, Jianghui and Li, Bing and Song, Siyang and Ghanem, Bernard},
+  journal={arXiv preprint arXiv:2505.21724},
+  year={2025}
+}}
+```
+
+## License
+
+This dataset is released under the **CC BY-NC 4.0** license.
+
+```
+```