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 .
 While virtual humans are proven tools for training, education and research, they are far from realizing their full potential. Advances are needed in individual capabilities, such as character animation and speech synthesis, but perhaps more importantly, fundamental questions remain as to how best to integrate these capabilities into a single framework that allows us to efficiently create characters that can engage users in meaningful and realistic social interactions. This integration requires in-depth, inter-disciplinary understanding few individuals, or even teams of individuals, possess. We help address this challenge by introducing the ICT Virtual Human Toolkit, which offers a flexible framework for exploring a variety of different types of virtual human systems, from virtual listeners and question-answering characters to virtual role-players. We show that due to its modularity, the Toolkit allows researchers to mix and match provided capabilities with their own, lowering the barrier of entry to this multi-disciplinary research challenge. Researchers demand much from their embodied conversational agents (ECAs), requiring them to be both life-like, as well as responsive to events in an interactive setting. We find that a flexible combination of animation approaches may be needed to satisfy these needs. In this paper we present SmartBody, an open source modular framework for animating ECAs in real time, based on the notion of hierarchically connected animation controllers. Controllers in SmartBody can employ arbitrary animation algorithms such as keyframe interpolation, motion capture or procedural animation. Controllers can also schedule or combine other controllers. We discuss our architecture in detail, including how we incorporate traditional approaches, and develop the notion of a controller as a reactive module within a generic framework, for realizing modular animation control. To illustrate the versatility of the architecture, we also discuss a range of applications that have used SmartBody successfully. This paper presents a simple method for "do as I do" motion transfer: given a source video of a person dancing, we can transfer that performance to a novel (amateur) target after only a few minutes of the target subject performing standard moves. We approach this problem as video-to-video translation using pose as an intermediate representation. To transfer the motion, we extract poses from the source subject and apply the learned pose-to-appearance mapping to generate the target subject. We predict two consecutive frames for temporally coherent video results and introduce a separate pipeline for realistic face synthesis. Although our method is quite simple, it produces surprisingly compelling results (see video). This motivates us to also provide a forensics tool for reliable synthetic content detection, which is able to distinguish videos synthesized by our system from real data. In addition, we release a first-of-its-kind open-source dataset of videos that can be legally used for training and motion transfer. Video Rewrite uses existing footage to create automatically new video of a person mouthing words that she did not speak in the original footage. This technique is useful in movie dubbing, for example, where the movie sequence can be modified to sync the actors’ lip motions to the new soundtrack. Video Rewrite automatically labels the phonemes in the training data and in the new audio track. Video Rewrite reorders the mouth images in the training footage to match the phoneme sequence of the new audio track. When particular phonemes are unavailable in the training footage, Video Rewrite selects the closest approximations. The resulting sequence of mouth images is stitched into the background footage. This stitching process automatically corrects for differences in head position and orientation between the mouth images and the background footage. Video Rewrite uses computer-vision techniques to track points on the speaker’s mouth in the training footage, and morphing techniques to combine these mouth gestures into the final video sequence. The new video combines the dynamics of the original actor’s articulations with the mannerisms and setting dictated by the background footage. Video Rewrite is the first facial-animation system to automate all the labeling and assembly tasks required to resync existing footage to a new soundtrack.