In dyadic interactions, humans communicate their intentions and state of mind using verbal and non-verbal cues, where multiple different facial reactions might be appropriate in response to a specific speaker behaviour. Then, how to develop a machine learning (ML) model that can automatically generate multiple appropriate, diverse, realistic and synchronised human facial reactions from an previously unseen speaker behaviour is a challenging task. Following the successful organisation of the first REACT challenge (REACT 2023), this edition of the challenge (REACT 2024) employs a subset used by the previous challenge, which contains segmented 30-secs dyadic interaction clips originally recorded as part of the NOXI and RECOLA datasets, encouraging participants to develop and benchmark Machine Learning (ML) models that can generate multiple appropriate facial reactions (including facial image sequences and their attributes) given an input conversational partner's stimulus under various dyadic video conference scenarios. This paper presents: (i) the guidelines of the REACT 2024 challenge; (ii) the dataset utilized in the challenge; and (iii) the performance of the baseline systems on the two proposed sub-challenges: Offline Multiple Appropriate Facial Reaction Generation and Online Multiple Appropriate Facial Reaction Generation, respectively. The challenge baseline code is publicly available at https://github.com/reactmultimodalchallenge/baseline_react2024.

翻译：在二元交互中，人类通过语言和非语言线索传达意图与心理状态，而针对特定说话者行为可能产生多种不同的面部反应。如何开发一种机器学习模型，能够从先前未见过的说话者行为中自动生成多种适切、多样、逼真且同步的人类面部反应，是一项具有挑战性的任务。继首届REACT挑战赛（REACT 2023）成功举办后，本届挑战赛（REACT 2024）沿用前届赛事的子数据集，该数据集包含从NOXI和RECOLA数据集中原始录制的、分段为30秒的二元交互片段，旨在鼓励参与者开发并基准化机器学习模型，使其在各类二元视频会议场景中，面对输入的对话伙伴刺激，能够生成多种适切的面部反应（包括面部图像序列及其属性）。本文介绍了：(i) REACT 2024挑战赛的指导原则；(ii) 挑战赛所使用的数据集；以及(iii) 基线系统在两个子挑战赛（离线多适切面部反应生成与在线多适切面部反应生成）中的性能表现。挑战赛基线代码已公开于https://github.com/reactmultimodalchallenge/baseline_react2024。