  The automatic co-speech gesture generation draws much attention in computer
animation. Previous works designed network structures on individual datasets,
which resulted in a lack of data volume and generalizability across different
motion capture standards. In addition, it is a challenging task due to the weak
correlation between speech and gestures. To address these problems, we present
UnifiedGesture, a novel diffusion model-based speech-driven gesture synthesis
approach, trained on multiple gesture datasets with different skeletons.
Specifically, we first present a retargeting network to learn latent
homeomorphic graphs for different motion capture standards, unifying the
representations of various gestures while extending the dataset. We then
capture the correlation between speech and gestures based on a diffusion model
architecture using cross-local attention and self-attention to generate better
speech-matched and realistic gestures. To further align speech and gesture and
increase diversity, we incorporate reinforcement learning on the discrete
gesture units with a learned reward function. Extensive experiments show that
UnifiedGesture outperforms recent approaches on speech-driven gesture
generation in terms of CCA, FGD, and human-likeness. All code, pre-trained
models, databases, and demos are available to the public at
https://github.com/YoungSeng/UnifiedGesture.

Human-Computer Interaction;Artificial Intelligence;Multimedia