76. Spatial Input and 3DUI
発表担当
目次
説明
VR interaction techniques define mappings between physical movements and virtual outcomes. While some mappings are learned through the adaptation of existing movement strategies, others require acquiring entirely new control policies. Drawing on motor learning theory, we introduce a design space that organizes these mappings into three families and provides a basis for reasoning about how the mappings are learned. To examine learning within individual families and their compositions, we start with two simple hand-based mappings, mirror reversal and cross-hand control, and their combination, allowing us to probe the design space in a controlled experiment with 96 participants. The mappings differ in initial difficulty, but participants achieve comparable overall learning. In the combined condition, prior exposure produces mapping specific start-up advantages. We discuss how this design space can support the analysis and design of VR mapping techniques.
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Zoos and aquariums often house animals in groups, making it difficult for visitors to recognize individuals. Penguins exemplify this challenge, even though identifying individuals can foster empathy and engagement. We addressed the following research questions: (RQ1) How do zoos and aquariums currently provide individual identification and what challenges remain? (RQ2) How does a sketch-based identification system affect visitor behavior and awareness in a real-world setting? To answer these questions, we (1) surveyed 25 facilities and clarified the limitations of existing practices, (2) refined a drawing-based retrieval algorithm to handle partial observations, (3) implemented a practical interactive system, and (4) validated it in a field study with 167 visitor groups (270 individuals). Results showed that the system encouraged name-based conversations, enhanced recognition of individual penguins, and increased dwell time. These findings suggest that sketch-based retrieval can enrich visitor experiences by promoting active observation and deeper engagement.
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Mid-air handwriting is a freeform text input modality that enables expression of individuality and creativity. In Mixed Reality, the recognition of writing strokes has conventionally depended on manual action or proximal planes. Such explicit reliance imposes cognitive load and quickly leads to fatigue. We present InkFlow, a novel bare-hand handwriting interaction approach that enables users to write continuously and naturally without explicit stroke control. We first design a user-friendly pipeline that leverages the widely adopted pinch–release gesture to intuitively collect annotated handwriting data. Next, we enhance a lightweight DS-TCN model with boundary-aware strategy to improve the learning of kinematic features. Moreover, building on cross-domain meta-learning, our approach achieves effective cross-user generalization and supports rapid personalization for new users. The comparative user study (N=30) shows the effectiveness and usability of our method and interaction design. A closed-loop online study (N=12) further demonstrates notable improvements in handwriting efficiency and physical comfort.
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AI assistants are transforming creative and knowledge domains, holding similar promise for mechanical design via 3D CAD software. Yet, current AI assistance for CAD relies on geometry or command history, lacking rich design intent. We investigate think-aloud computing as a lightweight approach to capture designers' spoken intent and inform how future AI assistance could leverage this to provide in-situ feedback. Through a three-part study with 10 designers and 10 experts, we (1) recorded designers' think-aloud verbalizations during 3D modelling, (2) compared expert feedback with and without think-aloud recordings, and (3) interviewed the original designers to evaluate feedback quality. Findings show that verbalizations surface rationale, future actions, and challenges --- insights absent from geometric and command data --- that enable feedback attuned to designers' goals. By harnessing think-aloud data, we uncover when to intervene, what to prompt, and characteristics of effective feedback, paving the way for context-aware AI assistance for CAD.
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While low-cost smartphone-based mobile VR (MVR) improves access to extended reality (XR) technology, it lacks the interaction capabilities of high-end devices. Following PRISMA 2020 methodology, we present a survey of both established and emerging MVR interaction techniques for travel, selection, manipulation, and system control. We reviewed literature from four databases published between 2011–2025 that reported evaluations of MVR interaction techniques. We filtered an initial set of 1041 publications to 64 articles and synthesized the current state of MVR interaction, focusing on cost-accessible approaches. We found many effective low-cost emerging selection and travel techniques, but low-cost object manipulation techniques remain problematic. Acoustic sensing offered superior 3D interaction performance than other sensing modalities while keeping cost low. Our findings inform a novel taxonomy of emerging MVR interaction techniques. We further present a toolkit supporting the design of cost-accessible XR interactions. Our findings underscore practical advantages of DIY approaches to future standalone XR applications developments.
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A VR body can move faster than its user, making actions like reaching more efficient. We propose a VR body that not only moves faster during reaching, but also starts moving before the user has decided which target to reach for. However, it is unclear whether such a VR body would speed up choices, since moving towards a wrong target might cause confusion, or influence users' choices. To explore these questions, we built a faster VR body prototype, focusing on an accelerated hand and arm, in a choice task. Thirty-four participants viewed random-dot displays to judge the overall motion direction and indicated their choice by pressing the corresponding button. Task difficulty was varied to influence choice uncertainty. Results showed that choice time decreased when the virtual body was 0.1 seconds ahead of the physical body, but increased when it was 0.3 seconds ahead. Users' choice distributions showed no significant differences.
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While car exterior designers are adopting 2D generative AI in their workflows, interior designers still face significant challenges. AI often fails to capture intended forms and layouts from interior sketches typically drawn in extreme perspectives and containing multiple components. Moreover, designers must rely on extensive manual work to explore numerous combinations of various components across different views. Thus, we propose a novel workflow in which designers sketch components individually, generate refined renderings for each component, and compose them to pre-visualize concepts. This pre-visualization then serves as input for generating high-quality renderings. We first interviewed interior designers to identify key challenges. We next developed a proof-of-concept system and conducted a pilot study to test its usefulness. We finally improved the system based on additional requirements and conducted a formal user study, demonstrating that our simple yet effective workflow supports designers in exploring, composing, and refining cabin designs into high-quality results.