Point & Grasp: Flexible Selection of Out-of-Reach Objects Through Probabilistic Cue IntegrationSelecting out-of-reach objects is a fundamental task in mixed reality (MR). Existing methods rely on a single cue or deterministically fuse multiple cues, leading to performance degradation when the dominant cue becomes unreliable. In this work, we introduce a probabilistic cue integration framework that enables flexible combination of multiple user-generated cues for intent inference. Inspired by natural grasping behavior, we instantiate the framework with pointing direction and grasp gestures as a new interaction technique, \textsc{Point\&Grasp}. To this end, we collect the \datasetfullname~(\dataset) dataset to train a robust likelihood model of the gestural cue, which captures grasping patterns not present in existing in-reach datasets. User studies demonstrate that our selection method with cue integration not only improves accuracy and speed over single-cue baselines, but also remains practically effective compared to state-of-the-art methods across various sources of ambiguity. The dataset and code are available at \url{https://github.com/drlxj/point-and-grasp}.2026XLXuejing Luo et al.Aalto UniversityFull-Body Interaction & Embodied InputMixed Reality WorkspacesPhysical-Digital Hybrid InteractionCHI
TingleTouch: Touch Guidance through Electrical Stimulation in Resistance TrainingIn resistance training, trainers employ touch guidance to help trainees control posture and activate muscles. Haptic feedback can extend this support to solitary workouts, but translating the nuances of touch into effective haptic patterns remains challenging. In this paper, we categorize the instructional messages conveyed through trainers' touch guidance and design electrical stimulation patterns to replicate them. A preliminary study with six trainers and six trainees identified six core messages underlying touch guidance. We then designed electrical stimulation patterns for each message and refined them with two sports scientists and a UX designer, ensuring usability and grounding. Finally, sixteen gymgoers evaluated these patterns in a controlled exercise task. Participants reliably distinguished the feedback and used the instructed muscles accordingly, achieving accuracies of 97.14% and 99.22% across two sessions, cross-checked with EMG and pose estimation. These findings demonstrate that the proposed electrical stimulation feedback is intuitive and learnable.2026DKDong-Uk Kim et al.Chung-Ang UniversityElectrical Muscle Stimulation (EMS)Fitness Tracking & Physical Activity MonitoringBehavior Change & Reflection TechnologyCHI
Moodialogue: Transforming Emotions into Personified, Conversational AgentsUnderstanding emotion for self-awareness requires recognizing not only its type and intensity but also the surrounding context and the insights it provides. While prior work has studied emotion recording, little attention has given to how such records might foster reflection on context. To address this gap, we developed Moodialogue, a system that enables users to personify emotion and engage in dialogue with it. In a six-week field study with nine participants, we found that personified emotion records supported dialogues that uncovered overlooked contexts and coexisting feelings beyond a single entry. Participants also reported savoring positive emotions more deeply and reframing negative ones from new perspectives. These findings point to design opportunities for systems that move beyond recording, enabling post-recording interactions that deepen reflection on emotional context and meaning.2026SJSangsu Jang et al.Chung-Ang UniversityEmpathy & Emotional DesignAffective Human-Computer DialogueBehavior Change & Reflection TechnologyCHI
Efficient Human-in-the-Loop Optimization via Priors Learned from User ModelsHuman-in-the-loop optimization identifies optimal interface designs by iteratively observing user performance. However, it often requires numerous iterations due to the lack of prior information. While recent approaches have accelerated this process by leveraging previous optimization data, collecting user data remains costly and often impractical. We present a conceptual framework, Human-in-the-Loop Optimization with Model-Informed Priors (HOMI), which augments human-in-the-loop optimization with a training phase where the optimizer learns adaptation strategies from diverse, synthetic user data generated with predictive models before deployment. To realize HOMI, we introduce Neural Acquisition Function+ (NAF+), a Bayesian optimization method featuring a neural acquisition function trained with reinforcement learning. NAF+ learns optimization strategies from large-scale synthetic data, improving efficiency in real-time optimization with users. We evaluate HOMI and NAF+ with mid-air keyboard optimization, a representative VR input task. Our work presents a new approach for more efficient interface adaptation by bridging in situ and in silico optimization processes.2026YLYi-Chi Liao et al.ETH ZürichMid-Air Haptics (Ultrasonic)Hand Gesture RecognitionImmersion & Presence ResearchCHI
TF-Shell: Facilitating Physical Deformation with Iterative and Shape Memory Thermoforming for 3D PrintingPrototyping with 3D printing depends heavily on virtual modeling, which requires expertise and often leads to scale mismatches and inefficient iteration. Physical deformation through heating is possible but challenging, as heating is difficult to control and deformation remains complex. We introduce TF-Shell, a thermoformable shell that enables repeatable, localized thermoforming of 3D-printed prototypes. Leveraging shape-memory properties, TF-Shell allows 3D-printed objects to achieve volumetric deformation, restoration, and shape memorization within the physical prototyping process. With customizable features, it can be embedded into free-form models through a design tool. A user study shows that TF-Shell provides intuitive, convenient physical modification and expands iteration beyond virtual modeling. Technical evaluations confirm its thermoformability and repeatability, establishing TF-Shell as a practical approach for integrating physical deformation into 3D printing workflows.2026DKDonghyeon Ko et al.School of ICT Convergence, University of UlsanShape-Changing Interfaces & Soft Robotic MaterialsCircuit Making & Hardware PrototypingCustomizable & Personalized ObjectsCHI
Modeling User Performance in Multi-Lane Moving-Target AcquisitionModern video games often feature moving target acquisition (MTA) tasks, where users must press a button when a moving target reaches an acquisition line. User performance models in MTA are useful for quantitative skill analysis and computational game level design, but have so far been constructed only for cases where there is a single lane for a target to appear and follow. In this study, the first user performance model is presented and validated for an MTA task with multiple lanes. The model is built as an integration of the existing MTA model and the drift-diffusion model, a model of human decision-making process under time-pressure. In a user study, we showed that the model can fit lane recognition error rates and input timing distributions with significantly higher coefficients of determination ($R^2$) and accuracy than a baseline model.2025JKJonghyun Kim et al.Yonsei UniversityVisualization Perception & CognitionSerious & Functional GamesCHI
MagPie: Extending a Smartphone’s Interaction Space via a Customizable Magnetic Back-of-Device Input AccessoryBack-of-Device (BoD) interfaces have emerged as a promising solution to free up screen real estate in smartphones by offloading interactions from the display to the back, thereby reducing reliance on on-screen interfaces. However, existing BoD solutions face limitations, such as requiring specialized hardware, consuming excessive power, or offering limited input vocabularies. We introduce MagPie, a novel BoD interface that leverages the magnetic phenomenon induced by MagSafe, part of the wireless charging standard. Users can seamlessly attach MagPie to MagSafe-enabled smartphones and interact using tangible, modular interfaces that generate unique magnetic signals upon activation. MagPie then detects these signals and recognizes the input through magnetic sensing. Our experiments with real-world users demonstrate that i) MagPie achieves high performance in accuracy, usability, deployability, responsiveness, and robustness across diverse environments, and ii) its tangible, intuitive, and customizable design opens up possibilities for a whole new class of smartphone interaction scenarios.2025IKInsu Kim et al.Chung-Ang University, Department of Smart CitiesShape-Changing Interfaces & Soft Robotic MaterialsContext-Aware ComputingCHI
CollageVis: Rapid Previsualization Tool for Indie Filmmaking using Video CollagesPrevisualization, previs, is essential for film production, allowing cinematographic experiments and effective collaboration. However, traditional previs methods like 2D storyboarding and 3D animation require substantial time, cost, and technical expertise, posing challenges for indie filmmakers. We introduce CollageVis, a rapid previsualization tool using video collages. CollageVis enables filmmakers to create previs through two main user interfaces. First, it automatically segments actors from videos and assigns roles using name tags, color filters, and face swaps. Second, it positions video layers on a virtual stage and allows users to record shots using mobile as a proxy for a virtual camera. These features were developed based on formative interviews by reflecting indie filmmakers’ needs and working methods. We demonstrate the system’s capability by replicating seven film scenes and evaluate the system’s usability with six indie filmmakers. The findings indicate that CollageVis allows more flexible yet expressive previs creation for idea development and collaboration.2024HJHye-Young Jo et al.Chung-Ang UniversityVideo Production & Editing3D Modeling & AnimationCHI
FlexBoard: A Flexible Breadboard for Interaction Prototyping on Curved and Deformable SurfacesWe present FlexBoard, an interaction prototyping platform that enables rapid prototyping with interactive components such as sensors, actuators and displays on curved and deformable objects. FlexBoard offers the rapid prototyping capabilities of traditional breadboards but is also flexible to conform to different shapes and materials. FlexBoard's bendability is enabled by replacing the rigid body of a breadboard with a flexible living hinge that holds the metal strips from a traditional breadboard while maintaining the standard pin spacing. In addition, FlexBoards are also shape-customizable as they can be cut to a specific length and joined together to form larger prototyping areas. We discuss FlexBoard's mechanical design and present a technical evaluation of its bendability, adhesion to curved and deformable surfaces, and holding force of electronic components. Finally, we show the usefulness of FlexBoard through 3 application scenarios with interactive textiles, curved tangible user interfaces, and VR.2023DKDonghyeon Ko et al.MIT CSAILShape-Changing Interfaces & Soft Robotic MaterialsDesktop 3D Printing & Personal FabricationCHI