Zero-Shot Style Transfer for Multimodal Data-Driven Gesture Synthesis

We propose a multimodal speech driven approach to generate 2D upper-body gestures for virtual agents, in the communicative style of different speakers, seen or unseen by our model during training. Upper-body gestures of a source speaker are generated based on the content of his/her multimodal data - speech acoustics and text semantics. The synthesized source speaker's gestures are conditioned on the multimodal style representation of the target speaker. Our approach is zero-shot, and can generalize the style transfer to new unseen speakers, without any additional training. An objective evaluation is conducted to validate our approach.