Emotions, shaped by past experiences, significantly influence decision-making and goal pursuit. Traditional cognitive-behavioral techniques for personal development rely on mental imagery to envision ideal selves, but may be less effective for individuals who struggle with visualization. This paper introduces Emotional Self-Voice (ESV), a novel system combining emotionally expressive language models and voice cloning technologies to render customized responses in the user’s own voice. We investigate the potential of ESV to nudge individuals towards their ideal selves in a study with 60 participants. Across all three conditions (ESV, text-only, and mental imagination), we observed an increase in resilience, confidence, motivation, and goal commitment, and the ESV condition was perceived as uniquely engaging and personalized. We discuss the implications of designing generated self-voice systems as a personalized behavioral intervention for different scenarios.

Learning a motor skill is essential for many aspects of our lives. The complexity of some of these activities makes it hard for novices to understand through observation. Calligraphy writing is one such artistic practice where learners compare the visual differences between their writing and expert manuscripts and adjust until they have achieved similar results. We propose an accessible plotter-based system that guides the learner’s arm and hand in three directions with an actuated brush. It converts static Chinese calligraphy manuscripts to G-code that reproduces the calligrapher’s movement. Through a user study with twelve novice calligraphy learners, we validated the efficacy of our system as a learning tool that allows novices to gain an intuition of nuanced skills such as depth variation more effectively compared to watching a video recording of the same movement.

We present Large Language Model for Mixed Reality (LLMR), a framework for the real-time creation and modification of interactive Mixed Reality experiences using LLMs. LLMR leverages novel strategies to tackle difficult cases where ideal training data is scarce, or where the design goal requires the synthesis of internal dynamics, intuitive analysis, or advanced interactivity. Our framework relies on text interaction and the Unity game engine. By incorporating techniques for scene understanding, task planning, self-debugging, and memory management, LLMR outperforms the standard GPT-4 by 4x in average error rate. We demonstrate LLMR’s cross-platform interoperability with several example worlds, and evaluate it on a variety of creation and modification tasks to show that it can produce and edit diverse objects, tools, and scenes. Finally, we conducted a usability study (N=11) with a diverse set that revealed participants had positive experiences with the system and would use it again.

We present a new and practical method for capturing user body pose in virtual reality experiences: integrating cameras into handheld controllers, where batteries, computation and wireless communication already exist. By virtue of the hands operating in front of the user during many VR interactions, our controller-borne cameras can capture a superior view of the body for digitization. Our pipeline composites multiple camera views together, performs 3D body pose estimation, uses this data to control a rigged human model with inverse kinematics, and exposes the resulting user avatar to end user applications. We developed a series of demo applications illustrating the potential of our approach and more leg-centric interactions, such as balancing games and kicking soccer balls. We describe our proof-of-concept hardware and software, as well as results from our user study, which point to imminent feasibility.

We describe how sheets of metalized mylar can be cut and then “inflated” into complex 3D forms with electrostatic charge for use in digitally-controlled, shape-changing displays. This is achieved by placing and nesting various cuts, slits and holes such that mylar elements repel from one another to reach an equilibrium state. Importantly, our technique is compatible with industrial and hobbyist cutting processes, from die and laser cutting to handheld exacto-knives and scissors. Given that mylar film costs <$1 per m2, we can create self-actuating 3D objects for just a few cents, opening new uses in low-cost consumer goods. We describe a design vocabulary, interactive simulation tool, fabrication guide, and proof-of-concept electrostatic actuation hardware. We detail our technique’s performance metrics along with qualitative feedback from a design study. We present numerous examples generated using our pipeline to illustrate the rich creative potential of our method.

Today’s consumer virtual reality (VR) systems offer immersive graphics and audio, but haptic feedback is rudimentary – delivered through controllers with vibration feedback or is non-existent (i.e., the hands operating freely in the air). In this paper, we explore an alternative, highly mobile and controller-free approach to haptics, where VR applications utilize the user’s own body to provide physical feedback. To achieve this, we warp (retarget) the locations of a user’s hands such that one hand serves as a physical surface or prop for the other hand. For example, a hand holding a virtual nail can serve as a physical backstop for a hand that is virtually hammering, providing a sense of impact in an air-borne and uninstrumented experience. To illustrate this rich design space, we implemented twelve interactive demos across three haptic categories. We conclude with a user study from which we draw design recommendations.