ZeroEGGS: Zero-shot Example-based Gesture Generation from Speech
We present ZeroEGGS, a neural network framework for speech-driven gesture
generation with zero-shot style control by example. This means style can be
controlled via only a short example motion clip, even for motion styles unseen
during training. Our model uses a Variational framework to learn a style
embedding, making it easy to modify style through latent space manipulation or
blending and scaling of style embeddings. The probabilistic nature of our
framework further enables the generation of a variety of outputs given the same
input, addressing the stochastic nature of gesture motion. In a series of
experiments, we first demonstrate the flexibility and generalizability of our
model to new speakers and styles. In a user study, we then show that our model
outperforms previous state-of-the-art techniques in naturalness of motion,
appropriateness for speech, and style portrayal. Finally, we release a
high-quality dataset of full-body gesture motion including fingers, with
speech, spanning across 19 different styles.