BodyFormer: Semantics-guided 3D Body Gesture Synthesis with Transformer
Kunkun Pang Dafei Qin Yingruo Fan Julian Habekost Takaaki Shiratori Junichi Yamagishi Taku Komura
Automatic gesture synthesis from speech is a topic that has attracted
researchers for applications in remote communication, video games and
Metaverse. Learning the mapping between speech and 3D full-body gestures is
difficult due to the stochastic nature of the problem and the lack of a rich
cross-modal dataset that is needed for training. In this paper, we propose a
novel transformer-based framework for automatic 3D body gesture synthesis from
speech. To learn the stochastic nature of the body gesture during speech, we
propose a variational transformer to effectively model a probabilistic
distribution over gestures, which can produce diverse gestures during
inference. Furthermore, we introduce a mode positional embedding layer to
capture the different motion speeds in different speaking modes. To cope with
the scarcity of data, we design an intra-modal pre-training scheme that can
learn the complex mapping between the speech and the 3D gesture from a limited
amount of data. Our system is trained with either the Trinity speech-gesture
dataset or the Talking With Hands 16.2M dataset. The results show that our
system can produce more realistic, appropriate, and diverse body gestures
compared to existing state-of-the-art approaches.