HaptoFlow: High-Fidelity Real-Time Vibrotactile Generation via Flow Matching for Virtual Reality
2026-08-03 • Human-Computer Interaction
Human-Computer Interaction
AI summaryⓘ
The authors created HaptoFlow, a new method to generate realistic touch sensations in virtual reality using a technique called Flow Matching. Their model learns how to produce detailed vibrations quickly, based on different materials and how you interact with them, like how fast or hard you stroke. Tests showed HaptoFlow makes more accurate vibrations and works faster than previous methods. People using the system noticed better touch quality without delays, making it useful for real-time VR experiences.
haptic feedbackvirtual realityvibrotactile generationFlow Matchingreal-time renderingmachine learningwaveform reproductioninteraction parameterslatencyuser study
Authors
Michikuni Eguchi, Yuichi Hiroi, Takefumi Hiraki
Abstract
Haptic feedback is widely employed to enhance immersion in Virtual Reality (VR) environments. However, designing haptic stimuli that cover diverse interaction conditions remains a significant scalability challenge. Data-driven haptic generation has emerged as a promising approach, yet existing models face an inherent trade-off between waveform expressiveness and inference responsiveness, which becomes increasingly critical as training data grow in scale and diversity. To address this challenge, we propose HaptoFlow, a vibrotactile generative model based on Flow Matching, designed for interactive real-time haptic rendering in VR. Flow Matching learns a continuous vector field that transforms a base distribution into the target data distribution, enabling efficient representation of complex haptic data distributions and thereby facilitating both high-quality generation and computational efficiency. We train HaptoFlow conditioned on material labels and interaction parameters (stroking velocity and applied force), and integrate it into a VR system. Technical evaluation demonstrates that HaptoFlow outperforms all baseline methods in both waveform reproduction accuracy and inference latency. Furthermore, user studies confirm that the system latency falls well within the perceptual threshold of visual-haptic delay, and statistically significant improvements in perceived haptic quality are observed for a subset of materials. These findings establish a practical foundation for scalable, data-driven haptic content creation in VR, and provide latency benchmarks that inform the design of future real-time haptic rendering systems. Project page: https://tamago117.github.io/HaptoFlow/.