REACT 2024: the Second Multiple Appropriate Facial Reaction Generation Challenge
University of Leicester（莱斯特大学）；Politecnico di Milano（米兰理工大学）；University of Cambridge（剑桥大学）；Monash University（莫纳什大学）；Universitat de Barcelona（巴塞罗那大学）；University of Nottingham（诺丁汉大学）；University of Augsburg（奥格斯堡大学）；Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）
Siyang Song, Micol Spitale, Cheng Luo, Cristina Palmero, German Barquero, Hengde Zhu, Sergio Escalera, Michel Valstar, Tobias Baur, Fabien Ringeval, Elisabeth Andre, Hatice Gunes
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.
REACT2023: the first Multi-modal Multiple Appropriate Facial Reaction Generation Challenge
University of Leicester（莱斯特大学）；University of Cambridge（剑桥大学）；Shenzhen University（深圳大学）；Universitat de Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；University of Nottingham（诺丁汉大学）；University of Augsburg（奥格斯堡大学）；Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）
REACT 2026: The Fourth Multiple Appropriate Facial Reaction Generation Challenge: Personalised MAFRG and Appropriate EEG Reaction Prediction
University of Exeter（埃克塞特大学）；Politecnico di Milano（米兰理工大学）；Nanjing University of Science and Technology（南京理工大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；King's College London（伦敦国王学院）；Universitat de Barcelona（巴塞罗那大学）；University of Nottingham（诺丁汉大学）；IMT Nord Europe（IMT 北欧欧洲）；Université Grenoble Alpes（格勒诺布尔-阿尔卑斯大学）；University of Augsburg（奥格斯堡大学）；University of Cambridge（剑桥大学）
REACT 2025: the Third Multiple Appropriate Facial Reaction Generation Challenge
University of Exeter（埃克塞特大学）；Politecnico di Milano（米兰理工大学）；University of Leicester（莱斯特大学）；King Abdullah University of Science and Technology（国王阿卜杜勒阿齐兹大学）；King’s College London（伦敦国王学院）；Universitat de Barcelona（巴塞罗那大学）；University of Nottingham（诺丁汉大学）；IMT Nord Europe（IMT北欧分校）；University of Augsburg（奥格斯堡大学）；Université Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；University of Cambridge（剑桥大学）
3D Facial Imperfection Regeneration: Deep learning approach and 3D printing prototypes
CIRTECH Institute（CIRTECH研究所）；HUTECH University（胡志明市工业大学）；Quy Nhon University（归仁大学）；China Medical University Hospital（中国医药大学附设医院）；China Medical University（中国医药大学）
DFME: A New Benchmark for Dynamic Facial Micro-expression Recognition
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；School of Computer Science and Technology, Southwest University of Science and Technology（西南科技大学计算机科学与技术学院）；School of Data Science, University of Science and Technology of China（中国科学技术大学数据科学学院）
ReactFace: Online Multiple Appropriate Facial Reaction Generation in Dyadic Interactions
Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院计算机视觉研究所）；Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University（深圳大学广东省智能信息处理重点实验室）；University of Nottingham Ningbo China（宁波诺丁汉大学）；University of Exeter（埃克塞特大学）；University of Cambridge（剑桥大学）
ReactDiff: Latent Diffusion for Facial Reaction Generation
School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University（生物医学工程学院及先进医学材料与器件国家重点实验室，上海科技大学）；School of Biomedical Engineering, Shanghai Jiao Tong University（生物医学工程学院，上海交通大学）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）
FERGI: Automatic Scoring of User Preferences for Text-to-Image Generation from Spontaneous Facial Expression Reaction
Neurosciences Graduate Program, University of California San Diego（加州大学圣地亚哥分校神经科学研究生项目）；Department of Computer Science and Engineering, University of California San Diego（加州大学圣地亚哥分校计算机科学与工程系）；Department of Cognitive Science, University of California San Diego（加州大学圣地亚哥分校认知科学系）；Halıcıoğlu Data Science Institute, University of California San Diego（加州大学圣地亚哥分校Halıcıoğlu数据科学研究所）
ECHO: Towards Emotionally Appropriate and Contextually Aware Interactive Head Generation
University of Exeter, Exeter, UK（埃克塞特大学）；Meituan, China（美团）；College of Computer Science, Nankai University, China（南开大学计算机学院）；School of Computer Science and Technology, Tongji University, China（同济大学计算机科学与技术学院）
