Ghorbani, SaeedFerstl, YlvaHolden, DanielTroje, Nikolaus F.Carbonneau, Marc‐AndréHauser, Helwig and Alliez, Pierre2023-03-222023-03-2220231467-8659https://doi.org/10.1111/cgf.14734https://diglib.eg.org:443/handle/10.1111/cgf14734We 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 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. Our code and data are publicly available at .CC BY-NC-ND Attribution-NonCommercial-NoDerivatives 4.0 Internationalanimationgesturescharacter controlmotion capture10.1111/cgf.14734206-216