REACT2023: the first Multi-modal Multiple Appropriate Facial Reaction
  Generation Challenge
Siyang Song Micol Spitale Cheng Luo German Barquero Cristina Palmero Sergio Escalera Michel Valstar Tobias Baur Fabien Ringeval Elisabeth Andre Hatice Gunes
The Multi-modal Multiple Appropriate Facial Reaction Generation Challenge
(REACT2023) is the first competition event focused on evaluating multimedia
processing and machine learning techniques for generating human-appropriate
facial reactions in various dyadic interaction scenarios, with all participants
competing strictly under the same conditions. The goal of the challenge is to
provide the first benchmark test set for multi-modal information processing and
to foster collaboration among the audio, visual, and audio-visual affective
computing communities, to compare the relative merits of the approaches to
automatic appropriate facial reaction generation under different spontaneous
dyadic interaction conditions. This paper presents: (i) novelties,
contributions and guidelines of the REACT2023 challenge; (ii) the dataset
utilized in the challenge; and (iii) the performance of 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
\url{https://github.com/reactmultimodalchallenge/baseline_react2023}.