HapticMatch: An Exploration for Generative Material Haptic Simulation and Interaction
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866917219486662656 |
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| author | Zhang, Mingxin Yao, Yu Makino, Yasutoshi Shinoda, Hiroyuki Sugiyama, Masashi |
| author_facet | Zhang, Mingxin Yao, Yu Makino, Yasutoshi Shinoda, Hiroyuki Sugiyama, Masashi |
| contents | High-fidelity haptic feedback is essential for immersive virtual environments, yet authoring realistic tactile textures remains a significant bottleneck for designers. We introduce HapticMatch, a visual-to-tactile generation framework designed to democratize haptic content creation. We present a novel dataset containing precisely aligned pairs of micro-scale optical images, surface height maps, and friction-induced vibrations for 100 diverse materials. Leveraging this data, we explore and demonstrate that conditional generative models like diffusion and flow-matching can synthesize high-fidelity, renderable surface geometries directly from standard RGB photos. By enabling a "Scan-to-Touch" workflow, HapticMatch allows interaction designers to rapidly prototype multimodal surface sensations without specialized recording equipment, bridging the gap between visual and tactile immersion in VR/AR interfaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_16639 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | HapticMatch: An Exploration for Generative Material Haptic Simulation and Interaction Zhang, Mingxin Yao, Yu Makino, Yasutoshi Shinoda, Hiroyuki Sugiyama, Masashi Human-Computer Interaction Databases High-fidelity haptic feedback is essential for immersive virtual environments, yet authoring realistic tactile textures remains a significant bottleneck for designers. We introduce HapticMatch, a visual-to-tactile generation framework designed to democratize haptic content creation. We present a novel dataset containing precisely aligned pairs of micro-scale optical images, surface height maps, and friction-induced vibrations for 100 diverse materials. Leveraging this data, we explore and demonstrate that conditional generative models like diffusion and flow-matching can synthesize high-fidelity, renderable surface geometries directly from standard RGB photos. By enabling a "Scan-to-Touch" workflow, HapticMatch allows interaction designers to rapidly prototype multimodal surface sensations without specialized recording equipment, bridging the gap between visual and tactile immersion in VR/AR interfaces. |
| title | HapticMatch: An Exploration for Generative Material Haptic Simulation and Interaction |
| topic | Human-Computer Interaction Databases |
| url | https://arxiv.org/abs/2601.16639 |