# REACT 2024: the Second Multiple Appropriate Facial Reaction Generation Challenge

**Authors:** Siyang Song, Micol Spitale, Cheng Luo, Cristina Palmero, German Barquero, Hengde Zhu, Sergio Escalera, Michel Valstar, Tobias Baur, Fabien Ringeval, Elisabeth Andre, Hatice Gunes  
**arXiv:** [2401.05166](https://arxiv.org/abs/2401.05166)  
**Date:** 2024-01-10  
**Summary page:** https://www.summarizepaper.com/en/paper/2401.05166v1/

## Key points

- The Second Multiple Appropriate Facial Reaction Generation Challenge (also known as the REACT challenge) focuses on the complex nature of human interactions.
- Humans communicate intentions and states of mind through both verbal and non-verbal cues.
- Multiple facial reactions may be appropriate in response to specific speaker behaviors, presenting a challenge for AI systems to generate diverse, realistic, and synchronized human facial expressions automatically.
- The challenge utilizes a subset of segmented 30-second dyadic interaction clips from the NOXI and RECOLA datasets.
- Participants are tasked with developing and benchmarking AI models capable of generating multiple appropriate facial reactions in various dyadic video conference scenarios.
- The challenge includes two sub-challenges: Offline Multiple Appropriate Facial Reaction Generation and Online Multiple Appropriate Facial Reaction Generation.
- Baseline systems showcased promising results, outperforming B Random, B Mime, B MeanSeq, and B MeanFr models in predicting meaningful human facial reactions across different speaker behaviors.

## Summary

The , also known as the Second Multiple Appropriate Facial Reaction Generation Challenge, focuses on the complex nature of . In these interactions, humans communicate intentions and states of mind through both verbal and non-verbal cues. However, in response to specific speaker behaviors, multiple facial reactions may be appropriate. This presents a challenge for  to automatically generate diverse, realistic, and synchronized human facial expressions. Building on the success of the previous REACT 2023 challenge, this edition utilizes a subset of segmented 30-second dyadic interaction clips from the NOXI and RECOLA datasets. Participants are tasked with developing and benchmarking  capable of generating multiple appropriate facial reactions in various dyadic video conference scenarios. The challenge includes two sub-challenges: Offline Multiple Appropriate Facial Reaction Generation and Online Multiple Appropriate Facial Reaction Generation. The guidelines of the challenge, details about the dataset used, and performance metrics of baseline systems are presented in this paper. The baseline systems showcased promising results, with all three baselines outperforming B Random, B Mime, B MeanSeq, and B MeanFr. This suggests that these models can predict meaningful human facial reactions across different speaker behaviors. In conclusion, in understanding and generating nuanced human facial expressions in response to various conversational stimuli.