HapticMatch: An Exploration for Generative Material Haptic Simulation and Interaction

TL;DR

HapticMatch is a framework that uses generative models to create realistic tactile textures from images, enabling easier and faster design of immersive haptic feedback in virtual environments.

Contribution

It introduces a novel dataset and a generative approach for converting visual images into tactile surface representations, facilitating democratized haptic content creation.

Findings

Conditional generative models can synthesize high-fidelity tactile textures from RGB images.

The framework enables rapid prototyping of haptic surfaces without specialized equipment.

HapticMatch bridges visual and tactile modalities for immersive VR/AR experiences.

Abstract

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.