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We propose a novel learned deep prior of body motion for 3D hand shapesynthesis and estimation in the domain of conversational gestures. Our modelbuilds upon the insight that body motion and hand gestures are stronglycorrelated in non-verbal communication settings. We formulate the learning ofthis prior as a prediction task of 3D hand shape over time given body motioninput alone. Trained with 3D pose estimations obtained from a large-scaledataset of internet videos, our hand prediction model produces convincing 3Dhand gestures given only the 3D motion of the speaker's arms as input. Wedemonstrate the efficacy of our method on hand gesture synthesis from bodymotion input, and as a strong body prior for single-view image-based 3D handpose estimation. We demonstrate that our method outperforms previousstate-of-the-art approaches and can generalize beyond the monologue-basedtraining data to multi-person conversations. Video results are available athttp://people.eecs.berkeley.edu/~evonne_ng/projects/body2hands/.

Body2Hands: Learning to Infer 3D Hands from Conversational Gesture Body Dynamics