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Although previous co-speech gesture generation methods are able to synthesizemotions in line with speech content, it is still not enough to handle diverseand complicated motion distribution. The key challenges are: 1) the one-to-manynature between the speech content and gestures; 2) the correlation modelingbetween the body joints. In this paper, we present a novel framework (EMoG) totackle the above challenges with denoising diffusion models: 1) To alleviatethe one-to-many problem, we incorporate emotion clues to guide the generationprocess, making the generation much easier; 2) To model joint correlation, wepropose to decompose the difficult gesture generation into two sub-problems:joint correlation modeling and temporal dynamics modeling. Then, the twosub-problems are explicitly tackled with our proposed Joint Correlation-awaretransFormer (JCFormer). Through extensive evaluations, we demonstrate that ourproposed method surpasses previous state-of-the-art approaches, offeringsubstantial superiority in gesture synthesis.

EMoG: Synthesizing Emotive Co-speech 3D Gesture with Diffusion Model