REACT 2025: the Third Multiple Appropriate Facial Reaction Generation Challenge

In dyadic interactions, a broad spectrum of human facial reactions might be appropriate for responding to each human speaker behaviour. Following the successful organisation of the REACT 2023 and REACT 2024 challenges, we are proposing the REACT 2025 challenge encouraging the development and benchmarking of Machine Learning (ML) models that can be used to generate multiple appropriate, diverse, realistic and synchronised human-style facial reactions expressed by human listeners in response to an input stimulus (i.e., audio-visual behaviours expressed by their corresponding speakers). As a key of the challenge, we provide challenge participants with the first natural and large-scale multi-modal Multiple Appropriate Facial Reaction Generation (MAFRG) dataset (called MARS) recording 136 human-human dyadic interactions containing a total of 2856 interaction sessions covering five different topics. In addition, this paper also presents the challenge guidelines and the performance of our baselines on the two proposed sub-challenges: Offline MAFRG and Online MAFRG, respectively. The challenge baseline code is publicly available at https://github.com/reactmultimodalchallenge/baseline_react2025

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Publication Details

Published
2025-10-25
DOI
https://doi.org/10.1145/3746027.3762244
Citations
2
Primary Topic
Face recognition and analysis
Type
article
Field-Weighted Citation Impact
2.55
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article

REACT 2025: the Third Multiple Appropriate Facial Reaction Generation Challenge

Tobias Baur, Mohamed Daoudi, Fabien Ringeval, Germán Barquero et al.
2 citations
Face recognition and analysis
2.55
article

REACT 2025: the Third Multiple Appropriate Facial Reaction Generation Challenge

Tobias Baur, Mohamed Daoudi, Fabien Ringeval, Germán Barquero, Micol Spitale, Sérgio Escalera, Andrew Howes, Siyang Song, Elisabeth André, Cristina Palmero, Cheng Luo, Hatice Güneş, Michel Valstar, Hengde Zhu, Xiangyu Kong
article en
2 citations

Abstract

In dyadic interactions, a broad spectrum of human facial reactions might be appropriate for responding to each human speaker behaviour. Following the successful organisation of the REACT 2023 and REACT 2024 challenges, we are proposing the REACT 2025 challenge encouraging the development and benchmarking of Machine Learning (ML) models that can be used to generate multiple appropriate, diverse, realistic and synchronised human-style facial reactions expressed by human listeners in response to an input stimulus (i.e., audio-visual behaviours expressed by their corresponding speakers). As a key of the challenge, we provide challenge participants with the first natural and large-scale multi-modal Multiple Appropriate Facial Reaction Generation (MAFRG) dataset (called MARS) recording 136 human-human dyadic interactions containing a total of 2856 interaction sessions covering five different topics. In addition, this paper also presents the challenge guidelines and the performance of our baselines on the two proposed sub-challenges: Offline MAFRG and Online MAFRG, respectively. The challenge baseline code is publicly available at https://github.com/reactmultimodalchallenge/baseline_react2025

University of Nottingham (GB), University of Leicester (GB), University of Augsburg (DE), King's College London (GB), University of Exeter (GB), University of Cambridge (GB), Centre Inria de l'Université de Lille (FR), King Abdullah University of Science and Technology (SA), Universitat de Barcelona (ES), Université Grenoble Alpes (FR), Politecnico di Milano (IT)
Openalex Percentile: Top 9%
Face recognition and analysis
2.55
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