REACT 2025: the Third Multiple Appropriate Facial Reaction Generation Challenge

TL;DR

REACT 2025 is a challenge that aims to develop and benchmark models for generating diverse, realistic, and synchronized facial reactions in response to speaker behaviors, using a new large-scale multi-modal dataset.

Contribution

It introduces the first large-scale multi-modal dataset (MARS) for facial reaction generation and provides benchmark guidelines for the REACT 2025 challenge.

Findings

Baseline models demonstrate the challenge's difficulty.

The dataset covers diverse dyadic interactions.

Benchmark results set a standard for future research.

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 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