Character animation remains challenging for novices and children despite advances in digital tools. While recent tangible interfaces have lowered barriers by enabling creators to animate their drawings on paper, they are limited to preset animation sequences and support for only human-like characters. We present Rig-a-Doodle, a tangible kit and web application for open-ended character rigging animation, where creators can draw any character and construct a custom physical rig using everyday materials to animate it. This work-in-progress contributes a system of tangible interaction to animate hand-drawn characters by direct physical manipulation of custom rigs in real-time. We share findings from a preliminary workshop with adults to explore the kinds of expressive animation the kit enables, discover issues with interaction, and source ideas for future directions.

Motion tracking to project users into embodied virtual reality (VR) as avatars is an essential application of real-time computer graphics. Most current embodied VR systems rely on head-mounted displays (HMDs) to estimate user pose, as headset sensors can track the head and hands, thereby reconstructing the full body without the need for external hardware. However, measuring the quality of motion reconstruction algorithms from HMD-based tracking, particularly those intended for use in social settings, remains challenging due to the complex interaction between motion and perceived social signals. This paper compares two industrial tracking reconstruction solutions, called HMD1 (i.e., a basic HMD-based method that uses head tracking and hand positions estimated from HMD cameras) and HMD2 (i.e., an advanced HMD-based method with additional onboard camera streams), that estimate user motion using only an HMD against ground-truth motion capture (MoCap) data. It advocates for a social signal-based analysis that views motion as a communication medium and employs user observations to measure whether viewers successfully perceive the information encoded in motion. Across 156 socially expressive clips, Social Signal ratings were more effective than generic measures at revealing differences between the HMD methods. HMD2 preserved social meaning more accurately than HMD1, with fewer significant deviations from MoCap, while both HMD methods were frequently rated less natural than MoCap. A qualitative review localized recurrent failure modes, such as arm swivel/shoulder errors, posture reconstruction issues, and floating/stance artifacts, which help explain the misreading of social signals. We release a dashboard scorecard, motion capture data, and a benchmark protocol to enable consistent motion evaluation. More generally, this work advocates for an underexplored approach to motion evaluation that focuses on assessing the semantics of motion to determine quality. As reliance on generative artificial intelligence (AI) increases, it is essential to standardize evaluation to preserve the authenticity of the social signals conveyed. The developed dataset and the evaluation framework are provided on our project's website: https://github.com/facebookresearch/MotionIsTheMessageDataset.

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.

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.

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 areplasticity, which includes extraversion and openness, andstability, 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.