2.5D effects, such as occlusion and perspective foreshortening, enhance visual dynamics and realism by introducing 3D depth cues into 2D designs. However, creating these effects remains challenging, as designers must manually infer and author depth relationships—such as relative ordering, occlusion boundaries, and perspective scaling—within 2D representations. We introduce DepthScape, a human–AI collaborative system that facilitates 2.5D effect creation by placing design elements directly into 3D reconstructions. Using monocular depth reconstruction, DepthScape transforms images into 3D scenes, enabling depth-based blending that produces realistic occlusion and perspective foreshortening. To simplify 3D placement, DepthScape leverages a vision-language model to analyze source images and extract key visual components as parametric anchors, which support direct manipulation editing. The system design was iteratively refined through a formative user study with an early prototype. We evaluate DepthScape through a technical study on 100 professional stock images to assess robustness, alongside an expert evaluation confirming design quality, usefulness, and broad application potential, further illustrated through five example scenarios.

With use, Accessibility Scout becomes an increasingly capable "accessibility scout", tailoring accessibility scans to an individual's mobility level, preferences, and specific environmental interests through collaborative Human-AI assessments.We present findings from three studies: a formative study with six participants to inform the design of Accessibility Scout, a technical evaluation of 500 images of built environments, and a user study with 10 participants of varying mobility.Results from our technical evaluation and user study show that Accessibility Scout can generate personalized accessibility scans that extend beyond traditional ADA considerations.Finally, we conclude with a discussion on the implications of our work and future steps for building more scalable and personalized accessibility assessments of the physical world.

Mapping, assessing, and creating personalized routes of indoor spaces for people with disabilities remains a grand challenge in accessibility research. Drawing on recent work in robotics as well as emergent work in smartphone-based mapping, we introduce RAIS (Robotic Accessibility Indoor Scanner), a robotic-based indoor mapping and accessibility assessment system. As a rapid prototype, RAIS is constructed with off-the-shelf components including a vacuum robot, smartphone, and phone gimbal along with a modified version of our previous LiDAR-based accessibility scannar RASSAR. In a preliminary evaluation of three indoor spaces, we demonstrate RAIS’s ability to autonomously scan spaces, produce detailed 3D reconstructions, and find and highlight accessibility issues.

The safety and accessibility of our homes is critical to quality of life and evolves as we age, become ill, host guests, or experience life events such as having children. Researchers and health professionals have created assessment instruments such as checklists that enable homeowners and trained experts to identify and mitigate safety and access issues. With advances in computer vision, augmented reality (AR), and mobile sensors, new approaches are now possible. We introduce RASSAR, a mobile AR application for semi-automatically identifying, localizing, and visualizing indoor accessibility and safety issues such as an inaccessible table height or unsafe loose rugs using LiDAR and real-time computer vision. We present findings from three studies: a formative study with 18 participants across five stakeholder groups to inform the design of RASSAR, a technical performance evaluation across ten homes demonstrating state-of-the-art performance, and a user study with six stakeholders. We close with a discussion of future AI-based indoor accessibility assessment tools, RASSAR’s extensibility, and key application scenarios.

Sound plays a crucial role in enhancing user experience and immersiveness in Augmented Reality (AR). However, current platforms lack support for AR sound authoring due to limited interaction types, challenges in collecting and specifying context information, and difficulty in acquiring matching sound assets. We present SonifyAR, an LLM-based AR sound authoring system that generates context-aware sound effects for AR experiences. SonifyAR expands the current design space of AR sound and implements a Programming by Demonstration (PbD) pipeline to automatically collect contextual information of AR events, including virtual-content-semantics and real-world context. This context information is then processed by a large language model to acquire sound effects with Recommendation, Retrieval, Generation, and Transfer methods. To evaluate the usability and performance of our system, we conducted a user study with eight participants and created five example applications, including an AR-based science experiment, and an assistive application for low-vision AR users.

In this demo paper, we introduce RASSAR, a mobile AR application for semi-automatically identifying, localizing, and visualizing indoor accessibility and safety issues using LiDAR and real-time computer vision. Our prototype supports four classes of detection problems: inaccessible object dimensions (e.g., table height), inaccessible object positions (e.g., a light switch out of reach), the presence of unsafe items (e.g., scissors), and the lack of proper assistive devices (e.g., grab bars). RASSAR’s design was informed by a formative interview study with 18 participants from five key stakeholder groups, including wheelchair users, blind and low vision participants, families with young children, and caregivers. Our envisioned use cases include vacation rental hosts, new caregivers, or people with disabilities themselves documenting issues in their homes or rental spaces and planning renovations. We present key findings from our formative interviews, the design of RASSAR, and results from an initial performance evaluation.

We present Kinergy—an interactive design tool for creating self-propelled motion by harnessing the energy stored in 3D printable springs. To produce controllable output motions, we introduce 3D printable kinetic units, a set of parameterizable designs that encapsulate 3D printable springs, compliant locks, and transmission mechanisms for three non-periodic motions—instant translation, instant rotation, continuous translation—and four periodic motions—continuous rotation, reciprocation, oscillation, intermittent rotation. Kinergy allows the user to create motion-enabled 3D models by embedding kinetic units, customize output motion characteristics by parameterizing embedded springs and kinematic elements, control energy by operating the specialized lock, and preview the resulting motion in an interactive environment. We demonstrate the potential of our techniques via example applications from spring-loaded cars to kinetic sculptures and close with a discussion of key challenges such as geometric constraints.