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ACM Multimedia 2025Grand Challenges

REACT 2025: the Third Multiple Appropriate Facial Reaction Generation Challenge

Siyang Song, Micol Spitale, Xiangyu Kong 0001, Hengde Zhu, Cheng Luo, Cristina Palmero, Germán Barquero, Sergio Escalera, Michel F. Valstar, Mohamed Daoudi, Tobias Baur 0001, Fabien Ringeval, Andrew Howes 0001, Elisabeth André, Hatice Gunes

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.1145/3746027.3762244 ↗

摘要

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