FusionProtor: A Mixed-Prototype Tool for Component-level Physical-to-Virtual 3D Transition and Simulation

要旨

Developing and simulating 3D prototypes is crucial in product conceptual design for ideation and presentation. Traditional methods often keep physical and virtual prototypes separate, leading to a disjointed prototype workflow. In addition, acquiring high-fidelity prototypes is time-consuming and resource-intensive, distracting designers from creative exploration. Recent advancements in generative artificial intelligence (GAI) and extended reality (XR) provided new solutions for rapid prototype transition and mixed simulation. We conducted a formative study to understand current challenges in the traditional prototype process and explore how to effectively utilize GAI and XR ability in prototype. Then we introduced FusionProtor, a mixed-prototype tool for component-level 3D prototype transition and simulation. We proposed a step-by-step generation pipeline in FusionProtor, effectively transiting 3D prototypes from physical to virtual and low- to high-fidelity for rapid ideation and iteration. We also innovated a component-level 3D creation method and applied it in XR environment for the mixed-prototype presentation and interaction. We conducted technical and user experiments to verify FusionProtor’s usability in supporting diverse designs. Our results verified that it achieved a seamless workflow between physical and virtual domains, enhancing efficiency and promoting ideation. We also explored the effect of mixed interaction on design and critically discussed its best practices for HCI community.

著者
Hongbo ZHANG
Zhejiang University, Hangzhou, Zhejiang, China
Pei Chen
Zhejiang University, Hangzhou, China
Xuelong Xie
School of Computer Science and Technology, Hangzhou, Zhejiang, China
Zhaoqu Jiang
Zhejiang University, Hangzhou, China
Yifei Wu
Zhejiang University, Hangzhou, China
Zejian Li
Zhejiang University, Ningbo, Zhejiang, China
Xiaoyu Chen
Zhejiang University, Hangzhou, China
Lingyun Sun
Zhejiang University, Hangzhou, China
DOI

10.1145/3706598.3713686

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713686

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Malleable and Adaptive Interface

G401
7 件の発表
2025-04-28 20:10:00
2025-04-28 21:40:00
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