  Audio-driven co-speech human gesture generation has made remarkable
advancements recently. However, most previous works only focus on single person
audio-driven gesture generation. We aim at solving the problem of
conversational co-speech gesture generation that considers multiple
participants in a conversation, which is a novel and challenging task due to
the difficulty of simultaneously incorporating semantic information and other
relevant features from both the primary speaker and the interlocutor. To this
end, we propose CoDiffuseGesture, a diffusion model-based approach for
speech-driven interaction gesture generation via modeling bilateral
conversational intention, emotion, and semantic context. Our method synthesizes
appropriate interactive, speech-matched, high-quality gestures for
conversational motions through the intention perception module and emotion
reasoning module at the sentence level by a pretrained language model.
Experimental results demonstrate the promising performance of the proposed
method.
