Automatic synthesis of realistic gestures promises to transform the fields of animation, avatars and communicative agents. In off‐line applications, novel tools can alter the role of an animator to that of a director, who provides only high‐level input for the desired animation; a learned network then translates these instructions into an appropriate sequence of body poses. In interactive scenarios, systems for generating natural animations on the fly are key to achieving believable and relatable characters. In this paper we address some of the core issues towards these ends. By adapting a deep learning‐based motion synthesis method called MoGlow, we propose a new generative model for generating state‐of‐the‐art realistic speech‐driven gesticulation. Owing to the probabilistic nature of the approach, our model can produce a battery of different, yet plausible, gestures given the same input speech signal. Just like humans, this gives a rich natural variation of motion. We additionally demonstrate the ability to exert directorial control over the output style, such as gesture level, speed, symmetry and spacial extent. Such control can be leveraged to convey a desired character personality or mood. We achieve all this without any manual annotation of the data. User studies evaluating upper‐body gesticulation confirm that the generated motions are natural and well match the input speech. Our method scores above all prior systems and baselines on these measures, and comes close to the ratings of the original recorded motions. We furthermore find that we can accurately control gesticulation styles without unnecessarily compromising perceived naturalness. Finally, we also demonstrate an application of the same method to full‐body gesticulation, including the synthesis of stepping motion and stance.

Fig. 1. Probabilistic motion generation. Random samples from our method can give many distinct output motions even if the input signal is the same.Data-driven modelling and synthesis of motion is an active research area with applications that include animation, games, and social robotics. This paper introduces a new class of probabilistic, generative, and controllable motion-data models based on normalising flows. Models of this kind can describe highly complex distributions, yet can be trained efficiently using exact maximum likelihood, unlike GANs or VAEs. Our proposed model is autoregressive and uses LSTMs to enable arbitrarily long time-dependencies. Importantly, is is also causal, meaning that each pose in the output sequence is generated without access to poses or control inputs from future time steps; this absence of algorithmic latency is important for interactive applications with real-time motion control. The approach can in principle be applied to any type of motion since it does not make restrictive, task-specific assumptions regarding the motion or the character morphology. We evaluate the models on motion-capture datasets of human and quadruped locomotion. Objective and subjective results show that randomly-sampled motion from the proposed method outperforms task-agnostic baselines and attains a motion quality close to recorded motion capture.

Embodied human communication encompasses both verbal (speech) and non-verbal information (e.g., gesture and head movements). Recent advances in machine learning have substantially improved the technologies for generating synthetic versions of both of these types of data: On the speech side, text-to-speech systems are now able to generate highly convincing, spontaneous-sounding speech using unscripted speech audio as the source material. On the motion side, probabilistic motion-generation methods can now synthesise vivid and lifelike speech-driven 3D gesticulation. In this paper, we put these two state-of-the-art technologies together in a coherent fashion for the first time. Concretely, we demonstrate a proof-of-concept system trained on a single-speaker audio and motion-capture dataset, that is able to generate both speech and full-body gestures together from text input. In contrast to previous approaches for joint speech-and-gesture generation, we generate full-body gestures from speech synthesis trained on recordings of spontaneous speech from the same person as the motion-capture data. We illustrate our results by visualising gesture spaces and textspeech-gesture alignments, and through a demonstration video. CCS CONCEPTS • Computing methodologies → Artificial intelligence.

No abstract

Passive optical motion capture is one of the predominant technologies for capturing high fidelity human motion, and is a workhorse in a large number of areas such as bio-mechanics, film and video games. While most state-of-the-art systems can automatically identify and track markers on the larger parts of the human body, the markers attached to the fingers and face provide unique challenges and usually require extensive manual cleanup. In this work we present a robust online method for identification and tracking of passive motion capture markers attached to non-rigid structures. The method is especially suited for large capture volumes and sparse marker sets. Once trained, our system can automatically initialize and track the markers, and the subject may exit and enter the capture volume at will. By using multiple assignment hypotheses and soft decisions, it can robustly recover from a difficult situation with many simultaneous occlusions and false observations (ghost markers). In three experiments, we evaluate the method for labeling a variety of marker configurations for finger and facial capture. We also compare the results with two of the most widely used motion capture platforms: Motion Analysis Cortex and Vicon Blade. The results show that our method is better at attaining correct marker labels and is especially beneficial for real-time applications.

QC 20171127

Dance requires skillful composition of complex movements that follow rhythmic, tonal and timbral features of music. Formally, generating dance conditioned on a piece of music can be expressed as a problem of modelling a high-dimensional continuous motion signal, conditioned on an audio signal. In this work we make two contributions to tackle this problem. First, we present a novel probabilistic autoregressive architecture that models the distribution over future poses with a normalizing flow conditioned on previous poses as well as music context, using a multimodal transformer encoder. Second, we introduce the currently largest 3D dance-motion dataset, obtained with a variety of motion-capture technologies, and including both professional and casual dancers. Using this dataset, we compare our new model against two baselines, via objective metrics and a user study, and show that both the ability to model a probability distribution, as well as being able to attend over a large motion and music context are necessary to produce interesting, diverse, and realistic dance that matches the music.

In order to ensure smooth turn-taking between conversational participants, interlocutors must have ways of providing information to one another about whether they have finished speaking or intend to continue. The current work investigates Swedish speakers' use of hand gestures in conjunction with turn change or turn hold in unrestricted, spontaneous speech. As has been reported by other researchers, we find that speakers' gestures end before the end of speech in cases of turn change, while they may extend well beyond the end of a given speech chunk in the case of turn hold. We investigate the degree to which prosodic cues and gesture cues to turn transition in Swedish face-to-face conversation are complementary versus functioning additively. The co-occurrence of acoustic prosodic features and gesture at potential turn boundaries gives strong support for considering hand gestures as part of the prosodic system, particularly in the context of discourse-level information such as maintaining smooth turn transition.

Unlike their human counterparts, arti cial agents such as robots and game characters may be deployed with a large variety of face and body con gurations. Some have articulated bodies but lack facial features, and others may be talking heads ending at the neck. Generally, they have many fewer degrees of freedom than humans through which they must express themselves, and there will inevitably be a ltering e ect when mapping human motion onto the agent. In this paper, we investigate ltering e ects on three types of embodiments, a) an agent with a body but no facial features, b) an agent with a head only and c) an agent with a body and a face. We performed a full performance capture of a mime actor enacting short interactions varying the non-verbal expression along ve dimensions (e.g. level of frustration and level of certainty) for each of the three embodiments. We performed a crowd sourced evaluation experiment comparing the video of the actor to the video of an animated robot for the di erent embodiments and dimensions. Our ndings suggest that the face is especially important to pinpoint emotional reactions, but is also most volatile to ltering e ects. e body motion on the other hand had more diverse interpretations, but tended to preserve the interpretation a er mapping, and thus proved to be more resilient to ltering.