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MRecGen: Multimodal Appropriate Reaction Generator

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arxiv 2307.02609 v1 pith:JJ57ZH6S submitted 2023-07-05 cs.CV

classification cs.CV
keywords appropriatereactionbehaviourgenerationhumanmrecgenmultimodalreactions
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Verbal and non-verbal human reaction generation is a challenging task, as different reactions could be appropriate for responding to the same behaviour. This paper proposes the first multiple and multimodal (verbal and nonverbal) appropriate human reaction generation framework that can generate appropriate and realistic human-style reactions (displayed in the form of synchronised text, audio and video streams) in response to an input user behaviour. This novel technique can be applied to various human-computer interaction scenarios by generating appropriate virtual agent/robot behaviours. Our demo is available at \url{https://github.com/SSYSteve/MRecGen}.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ARIG: Autoregressive Interactive Head Generation for Real-time Conversations

    cs.CV 2025-07 conditional novelty 6.0 of 10

    ARIG introduces a real-time, frame-wise autoregressive head generation framework with diffusion-based continuous motion prediction, improving interactive realism over clip-wise methods.

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