We present AutoOptimization, a novel multi-objective optimization framework for adapting user interfaces. From a user’s verbal preferences for changing a UI, our framework guides a prioritization-based Pareto frontier search over candidate layouts. It selects suitable objective functions for UI placement while simultaneously parameterizing them according to the user’s instructions to define the optimization problem. A solver then generates a series of optimal UI layouts, which our framework validates against the user’s instructions to adapt the UI with the final solution. Our approach thus overcomes the previous need for manual inspection of layouts and the use of population averages for objective parameters. We integrate multiple agents sequentially within our framework, enabling the system to leverage their reasoning capabilities to interpret user preferences, configure the optimization problem, and validate optimization outcomes. We evaluate each step of our framework inside a Mixed Reality use case and demonstrate that AutoOptimization effectively increases the usability of UI adaptation schemes.

Generative models are increasingly powerful, yet users struggle to guide them through prompts. The generative process is difficult to control and unpredictable, and user instructions may be ambiguous or under-specified. Prior prompt refinement tools heavily rely on human effort, while prompt optimization methods focus on numerical functions and are not designed for human-centered generative tasks, where feedback is better expressed as binary preferences and demands convergence within few iterations. We present APPO, a preference-guided prompt optimization algorithm. Instead of iterating prompts, users only provide binary preferential feedback. APPO adaptively balances its strategies between exploiting user feedback and exploring new directions, yielding effective and efficient optimization. We evaluate APPO on image generation, and the results show APPO enables achieving satisfactory outcomes in fewer iterations with lower cognitive load than manual prompt editing. We anticipate APPO will advance human-AI collaboration in generative tasks by leveraging user preferences to guide complex content creation.

Optimal input settings vary across users due to differences in motor abilities and personal preferences, which are typically addressed by manual tuning or calibration. Although human-in-the-loop optimization has the potential to identify optimal settings during use, it is rarely applied due to its long optimization process. A more efficient approach would continually leverage data from previous users to accelerate optimization, exploiting shared traits while adapting to individual characteristics. We introduce the concept of Continual Human-in-the-Loop Optimization and a Bayesian optimization-based method that leverages a Bayesian-neural-network surrogate model to capture population-level characteristics while adapting to new users. We propose a generative replay strategy to mitigate catastrophic forgetting. We demonstrate our method by optimizing virtual reality keyboard parameters for text entry using direct touch, showing reduced adaptation times with a growing user base. Our method opens the door for next-generation personalized input systems that improve with accumulated experience.

Adjusting visual parameters such as brightness and contrast is common in our everyday experiences. Finding the optimal parameter setting is challenging due to the large search space and the lack of an explicit objective function, leaving users to rely solely on their implicit preferences. Prior work has explored Preferential Bayesian Optimization (PBO) to address this challenge, involving users to iteratively select preferred designs from candidate sets. However, PBO often requires many rounds of preference comparisons, making it more suitable for designers than everyday end-users. We propose Meta-PO, a novel method that integrates PBO with meta-learning to improve sample efficiency. Specifically, Meta-PO infers prior users' preferences and stores them as models, which are leveraged to intelligently suggest design candidates for the new users, enabling faster convergence and more personalized results. An experimental evaluation of our method for appearance design tasks on 2D and 3D content showed that participants achieved satisfactory appearance in 5.86 iterations using Meta-PO when participants shared similar goals with a population (e.g., tuning for a "warm"look) and in 8 iterations even generalizes across divergent goals (e.g., from "vintage", "warm", to "holiday"). Meta-PO makes personalized visual optimization more applicable to end-users through a generalizable, more efficient optimization conditioned on preferences, with the potential to scale interface personalization more broadly.

While Virtual Reality (VR) systems can present virtual elements such as notifications anywhere, designing them so they are not missed by or distracting to users is highly challenging for content creators. To address this challenge, we introduce a novel approach to predict the noticeability of virtual elements. It computes the visual saliency distribution of what users see, and analyzes the temporal changes of the distribution with respect to the dynamic virtual elements that are animated. The computed features serve as input for a long short-term memory (LSTM) model that predicts whether a virtual element will be noticed. Our approach is based on data collected from 24 users in different VR environments performing tasks such as watching a video or typing. We evaluate our approach (n = 12), and show that it can predict the timing of when users notice a change to a virtual element within 2.56 sec compared to a ground truth, and demonstrate the versatility of our approach with a set of applications. We believe that our predictive approach opens the path for computational design tools that assist VR content creators in creating interfaces that automatically adapt virtual elements based on noticeability.

Despite a rich history of investigating smartphone overuse intervention techniques, AI-based just-in-time adaptive intervention (JITAI) methods for overuse reduction are lacking. We develop Time2Stop, an intelligent, adaptive, and explainable JITAI system that leverages machine learning to identify optimal intervention timings, introduces interventions with transparent AI explanations, and collects user feedback to establish a human-AI loop and adapt the intervention model over time. We conducted an 8-week field experiment (N=71) to evaluate the effectiveness of both the adaptation and explanation aspects of Time2Stop. Our results indicate that our adaptive models significantly outperform the baseline methods on intervention accuracy (>32.8% relatively) and receptivity (>8.0%). In addition, incorporating explanations further enhances the effectiveness by 53.8% and 11.4% on accuracy and receptivity, respectively. Moreover, Time2Stop significantly reduces overuse, decreasing app visit frequency by 7.0 ∼ 8.9%. Our subjective data also echoed these quantitative measures. Participants preferred the adaptive interventions and rated the system highly on intervention time accuracy, effectiveness, and level of trust. We envision our work can inspire future research on JITAI systems with a human-AI loop to evolve with users.

Mixed Reality is increasingly used in mobile settings beyond controlled home and office spaces. This mobility introduces the need for user interface layouts that adapt to varying contexts. However, existing adaptive systems are designed only for static environments. In this paper, we introduce SituationAdapt, a system that adjusts Mixed Reality UIs to real-world surroundings by considering environmental and social cues in shared settings. Our system consists of perception, reasoning, and optimization modules for UI adaptation. Our perception module identifies objects and individuals around the user, while our reasoning module leverages a Vision-and-Language Model to assess the placement of interactive UI elements. This ensures that adapted layouts do not obstruct relevant environmental cues or interfere with social norms. Our optimization module then generates Mixed Reality interfaces that account for these considerations as well as temporal constraints. For evaluation, we first validate our reasoning module’s capability of assessing UI contexts in comparison to human expert users. In an online user study, we then establish SituationAdapt’s capability of producing context-aware layouts for Mixed Reality, where it outperformed previous adaptive layout methods. We conclude with a series of applications and scenarios to demonstrate SituationAdapt’s versatility.

We propose HandAvatar to enable users to embody non-humanoid avatars using their hands. HandAvatar leverages the high dexterity and coordination of users’ hands to control virtual avatars, enabled through our novel approach for automatically-generated joint-to-joint mappings. We contribute an observation study to understand users’ preferences on hand-to-avatar mappings on eight avatars. Leveraging insights from the study, we present an automated approach that generates mappings between users’ hands and arbitrary virtual avatars by jointly optimizing control precision, structural similarity, and comfort. We evaluated HandAvatar on static posing, dynamic animation, and creative exploration tasks. Results indicate that HandAvatar enables more precise control, requires less physical effort, and brings comparable embodiment compared to a state-of-the-art body-to-avatar control method. We demonstrate HandAvatar’s potential with applications including non-humanoid avatar based social interaction in VR, 3D animation composition, and VR scene design with physical proxies. We believe that HandAvatar unlocks new interaction opportunities, especially for usage in Virtual Reality, by letting users become the avatar in applications including virtual social interaction, animation, gaming, or education.

We present digital mechanical metamaterials that enable multiple computation loops and reprogrammable logic functions, making a significant step towards passive yet interactive devices. Our materials consist of many cells that transmit signals using an embedded bistable spring. When triggered, the bistable spring displaces and triggers the next cell. We integrate a recharging mechanism to recharge the bistable springs, enabling multiple computation rounds. Between the iterations, we enable reprogramming the logic functions after fabrication. We demonstrate that such materials can trigger a simple controlled actuation anywhere in the material to change the local shape, texture, stiffness, and display. This enables large-scale interactive and functional materials with no or a small number of external actuators. We showcase the capabilities of our system with various examples: a haptic floor with tunable stiffness for different VR scenarios, a display with easy-to-reconfigure messages after fabrication, or a tactile notification integrated into users’ desktops.

Despite significant improvements to Virtual Reality (VR) technologies, most VR displays are fixed focus and depth perception is still a key issue that limits the user experience and the interaction performance. To supplement humans’ inherent depth cues (e.g., retinal blur, motion parallax), we investigate users’ perceptual mappings of distance to virtual objects’ appearance to generate visual cues aimed to enhance depth perception. As a first step, we explore color-to-depth mappings for virtual objects so that their appearance differs in saturation and value to reflect their distance. Through a series of controlled experiments, we elicit and analyze users’ strategies of mapping a virtual object’s hue, saturation, value and a combination of saturation and value to its depth. Based on the collected data, we implement a computational model that generates color-to-depth mappings fulfilling adjustable requirements on confusion probability, number of depth levels, and consistent saturation/value changing tendency. We demonstrate the effectiveness of color-to-depth mappings in a 3D sketching task, showing that compared to single-colored targets and strokes, with our mappings, the users were more confident in the accuracy without extra cognitive load and reduced the perceived depth error by 60.8%. We also implement four VR applications and demonstrate how our color cues can benefit the user experience and interaction performance in VR.

Head-mounted Mixed Reality (MR) systems enable touch in­teraction on any physical surface. However, optical methods (i.e., with cameras on the headset) have difficulty in determin­ing the touch contact accurately. We show that a finger ring with Inertial Measurement Unit (IMU) can substantially im­prove the accuracy of contact sensing from 84.74% to 98.61% (f1 score), with a low latency of 10 ms. We tested different ring wearing positions and tapping postures (e.g., with different fingers and parts). Results show that an IMU-based ring worn on the proximal phalanx of the index finger can accurately sense touch contact of most usable tapping postures. Partici­pants preferred wearing a ring for better user experience. Our approach can be used in combination with the optical touch sensing to provide robust and low-latency contact detection.