Human speech is often accompanied by hand and arm gestures. Given audio speech input, we generate plausible gestures to go along with the sound. Specifically, we perform cross-modal translation from "in-the-wild'' monologue speech of a single speaker to their hand and arm motion. We train on unlabeled videos for which we only have noisy pseudo ground truth from an automatic pose detection system. Our proposed model significantly outperforms baseline methods in a quantitative comparison. To support research toward obtaining a computational understanding of the relationship between gesture and speech, we release a large video dataset of person-specific gestures. The project website with video, code and data can be found at this http URL .
In this paper, we introduce Inverse-Foley Animation, a technique for optimizing rigid-body animations so that contact events are synchronized with input sound events. A precomputed database of randomly sampled rigid-body contact events is used to build a contact-event graph, which can be searched to determine a plausible sequence of contact events synchronized with the input sound's events. To more easily find motions with matching contact times, we allow transitions between simulated contact events using a motion blending formulation based on modified contact impulses. We fine tune synchronization by slightly retiming ballistic motions. Given a sound, our system can synthesize synchronized motions using graphs built with hundreds of thousands of precomputed motions, and millions of contact events. Our system is easy to use, and has been used to plan motions for hundreds of sounds, and dozens of rigid-body models. We present a method that gets as input an audio of violin or piano playing, and outputs a video of skeleton predictions which are further used to animate an avatar. The key idea is to create an animation of an avatar that moves their hands similarly to how a pianist or violinist would do, just from audio. Notably, it's not clear if body movement can be predicted from music at all and our aim in this work is to explore this possibility. In this paper, we present the first result that shows that natural body dynamics can be predicted. We built an LSTM network that is trained on violin and piano recital videos uploaded to the Internet. The predicted points are applied onto a rigged avatar to create the animation.