ARIG introduces a real-time, frame-wise autoregressive head generation framework with diffusion-based continuous motion prediction, improving interactive realism over clip-wise methods.
MRecGen: Multimodal Appropriate Reaction Generator
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abstract
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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ARIG: Autoregressive Interactive Head Generation for Real-time Conversations
ARIG introduces a real-time, frame-wise autoregressive head generation framework with diffusion-based continuous motion prediction, improving interactive realism over clip-wise methods.