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In dyadic interactions, humans communicate their intentions and state of mindusing verbal and non-verbal cues, where multiple different facial reactionsmight be appropriate in response to a specific speaker behaviour. Then, how todevelop a machine learning (ML) model that can automatically generate multipleappropriate, diverse, realistic and synchronised human facial reactions from anpreviously unseen speaker behaviour is a challenging task. Following thesuccessful organisation of the first REACT challenge (REACT 2023), this editionof the challenge (REACT 2024) employs a subset used by the previous challenge,which contains segmented 30-secs dyadic interaction clips originally recordedas part of the NOXI and RECOLA datasets, encouraging participants to developand benchmark Machine Learning (ML) models that can generate multipleappropriate facial reactions (including facial image sequences and theirattributes) given an input conversational partner's stimulus under variousdyadic video conference scenarios. This paper presents: (i) the guidelines ofthe 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 MultipleAppropriate Facial Reaction Generation, respectively. The challenge baselinecode is publicly available athttps://github.com/reactmultimodalchallenge/baseline_react2024.

REACT 2024: the Second Multiple Appropriate Facial Reaction Generation Challenge