REACT 2025: the Third Multiple Appropriate Facial Reaction Generation Challenge
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2025
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| author | Song, Siyang Spitale, Micol Kong, Xiangyu Zhu, Hengde Luo, Cheng Palmero, Cristina Barquero, German Escalera, Sergio Valstar, Michel Daoudi, Mohamed Baur, Tobias Ringeval, Fabien Howes, Andrew Andre, Elisabeth Gunes, Hatice |
| author_facet | Song, Siyang Spitale, Micol Kong, Xiangyu Zhu, Hengde Luo, Cheng Palmero, Cristina Barquero, German Escalera, Sergio Valstar, Michel Daoudi, Mohamed Baur, Tobias Ringeval, Fabien Howes, Andrew Andre, Elisabeth Gunes, Hatice |
| contents | 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 MAFRG dataset (called MARS) recording 137 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17223 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | REACT 2025: the Third Multiple Appropriate Facial Reaction Generation Challenge Song, Siyang Spitale, Micol Kong, Xiangyu Zhu, Hengde Luo, Cheng Palmero, Cristina Barquero, German Escalera, Sergio Valstar, Michel Daoudi, Mohamed Baur, Tobias Ringeval, Fabien Howes, Andrew Andre, Elisabeth Gunes, Hatice Computer Vision and Pattern Recognition 68T40 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 MAFRG dataset (called MARS) recording 137 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 |
| title | REACT 2025: the Third Multiple Appropriate Facial Reaction Generation Challenge |
| topic | Computer Vision and Pattern Recognition 68T40 |
| url | https://arxiv.org/abs/2505.17223 |