Embodied virtual reality faithfully renders users' movements onto an avatar in a virtual 3D environment, supporting nuanced nonverbal behavior alongside verbal communication. To investigate communication behavior within this medium, we had 30 dyads complete two tasks using a shared visual workspace: negotiating an apartment layout and placing model furniture on an apartment floor plan. Dyads completed both tasks under three different conditions: face-to-face, embodied VR with visible full-body avatars, and no embodiment VR, where the participants shared a virtual space, but had no visible avatars. Both subjective measures of users' experiences and detailed annotations of verbal and nonverbal behavior are used to understand how the media impact communication behavior. Embodied VR provides a high level of social presence with conversation patterns that are very similar to face-to-face interaction. In contrast, providing only the shared environment was generally found to be lonely and appears to lead to degraded communication.

Style is an intrinsic, inescapable part of human motion. It complements the content of motion to convey meaning, mood, and personality. Existing state-of-the-art motion style methods require large quantities of example data and intensive computational resources at runtime. To ensure output quality, such style transfer applications are often run on desktop machine with GPUs and significant memory. In this paper, we present a fast and expressive neural network-based motion style transfer method that generates stylized motion with quality comparable to the state of the art method, but uses much less computational power and a much smaller memory footprint. Our method also allows the output to be adjusted in a latent style space, something not offered in previous approaches. Our style transfer model is implemented using three multi-layered networks: a pose network, a timing network and a foot-contact network. A one-hot style vector serves as an input control knob and determines the stylistic output of these networks. During training, the networks are trained with a large motion capture database containing heterogeneous actions and various styles. Joint information vectors together with one-hot style vectors are extracted from motion data and fed to the networks. Once the network has been trained, the database is no longer needed on the device, thus removing the large memory requirement of previous motion style methods. At runtime, our model takes novel input and allows real-valued numbers to be specified in the style vector, which can be used for interpolation, extrapolation or mixing of styles. With much lower memory and computational requirements, our networks are efficient and fast enough for real-time use on mobile devices. Requiring no information about future states, the style transfer can be performed in an online fashion. We validate our result both quantitatively and perceptually, confirming its effectiveness and improvement over previous approaches.

Applications such as virtual tutors, games, and natural interfaces increasingly require animated characters to take on social roles while interacting with humans. The effectiveness of these applications depends on our ability to control the social presence of characters, including their personality. Understanding how movement impacts the perception of personality allows us to generate characters more capable of fulfilling this social role. The two studies described herein focus on gesture as a key component of social communication and examine how a set of gesture edits, similar to the types of changes that occur during motion warping, impact the perceived personality of the character. Surprisingly, when based on thin-slice gesture data, people's judgments of character personality mainly fall in a 2D subspace rather than independently impacting the full set of traits in the standard Big Five model of personality. These two dimensions are plasticity , which includes extraversion and openness, and stability , which includes emotional stability, agreeableness, and conscientiousness. A set of motion properties is experimentally determined that impacts each of these two traits. We show that when these properties are systematically edited in new gesture sequences, we can independently influence the character's perceived stability and plasticity (and the corresponding Big Five traits), to generate distinctive personalities. We identify motion adjustments salient to each judgment and, in a series of perceptual studies, repeatedly generate four distinctly perceived personalities. The effects extend to novel gesture sequences and character meshes, and even largely persist in the presence of accompanying speech. This paper furthers our understanding of how gesture can be used to control the perception of personality and suggests both the potential and possible limits of motion editing approaches.

Children’s drawings have a wonderful inventiveness, creativity, and variety to them. We present a system that automatically animates children’s drawings of the human figure, is robust to the variance inherent in these depictions, and is simple and straightforward enough for anyone to use. We demonstrate the value and broad appeal of our approach by building and releasing the Animated Drawings Demo, a freely available public website that has been used by millions of people around the world. We present a set of experiments exploring the amount of training data needed for fine-tuning, as well as a perceptual study demonstrating the appeal of a novel twisted perspective retargeting technique. Finally, we introduce the Amateur Drawings Dataset, a first-of-its-kind annotated dataset, collected via the public demo, containing over 178,000 amateur drawings and corresponding user-accepted character bounding boxes, segmentation masks, and joint location annotations.