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

Github Project

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

@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.