mlsanityData to 14 Oct 2026

Multiple Appropriate Facial Reaction Generation in Dyadic Interaction Settings: What, Why and How?

arXiv 2302.06514 ↗First posted 13 Feb 2023cs.CVcited by 16

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Abstract (arXiv)

According to the Stimulus Organism Response (SOR) theory, all human behavioral reactions are stimulated by context, where people will process the received stimulus and produce an appropriate reaction. This implies that in a specific context for a given input stimulus, a person can react differently according to their internal state and other contextual factors. Analogously, in dyadic interactions, humans communicate using verbal and nonverbal cues, where a broad spectrum of listeners' non-verbal reactions might be appropriate for responding to a specific speaker behaviour. There already exists a body of work that investigated the problem of automatically generating an appropriate reaction for a given input. However, none attempted to automatically generate multiple appropriate reactions in the context of dyadic interactions and evaluate the appropriateness of those reactions using objective measures. This paper starts by defining the facial Multiple Appropriate Reaction Generation (fMARG) task for the first time in the literature and proposes a new set of objective evaluation metrics to evaluate the appropriateness of the generated reactions. The paper subsequently introduces a framework to predict, generate, and evaluate multiple appropriate facial reactions.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 8 of its 28 references that are in the map. First posted on arXiv 13 Feb 2023.

Builds on

8 of 28 references.

References · 8

  1. 2018Deep Facial Expression Recognition: A Surveycited by 270
  2. 2022Learning to Listen: Modeling Non-Deterministic Dyadic Facial Motioncited by 126
  3. 2020Gesticulator: A framework for semantically-aware speech-driven gesture generationcited by 84
  4. 2022Learning Multi-dimensional Edge Feature-based AU Relation Graph for Facial Action Unit Recognitioncited by 82
  5. 2019Exploiting Persona Information for Diverse Generation of Conversational Responsescited by 58
  6. 2022The GENEA Challenge 2022: A large evaluation of data-driven co-speech gesture generationcited by 40
  7. 2020Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Datasetcited by 31
  8. 2022GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge Featurescited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 5; 2026: 4.

16 papers.

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  1. 2023ReactFace: Online Multiple Appropriate Facial Reaction Generation in Dyadic Interactionscited by 17
  2. 2024REACT 2024: the Second Multiple Appropriate Facial Reaction Generation Challengecited by 16
  3. 2023Reversible Graph Neural Network-based Reaction Distribution Learning for Multiple Appropriate Facial Reactions Generationcited by 11
  4. 2025REACT 2025: the Third Multiple Appropriate Facial Reaction Generation Challengecited by 9
  5. 2023REACT2023: the first Multi-modal Multiple Appropriate Facial Reaction Generation Challengecited by 5
  6. 2025OmniResponse: Online Multimodal Conversational Response Generation in Dyadic Interactionscited by 5
  7. 2024Robust Facial Reactions Generation: An Emotion-Aware Framework with Modality Compensationcited by 4
  8. 2025ReactDiff: Fundamental Multiple Appropriate Facial Reaction Diffusion Modelcited by 4
  9. 2025Latent Behavior Diffusion for Sequential Reaction Generation in Dyadic Settingcited by 3
  10. 2023MRecGen: Multimodal Appropriate Reaction Generatorcited by 2
  11. 2025Multimodal Quantitative Measures for Multiparty Behaviour Evaluationcited by 2
  12. 2024PainDiffusion: Learning to Express Paincited by 0
4 more
  1. 2026REACT 2026: The Fourth Multiple Appropriate Facial Reaction Generation Challenge: Personalised MAFRG and Appropriate EEG Reaction Predictioncited by 0
  2. 2026ResDiffFRG: Residual Diffusion for Multiple Appropriate Facial Reaction Generationcited by 0
  3. 2026Beyond End-to-End Black Box Mapping: An Intentional Agent Framework for Cognitive-driven Facial Reaction Generationcited by 0
  4. 2026GLARE: Generating Listening Heads with Appropriate Reactionscited by 0