AI 中文总结
该研究提出基于Flow Matching的HaptoFlow振动触觉生成模型，用于VR实时触觉渲染，其在波形精度、推理延迟及用户感知质量上均优于基线方法，为VR可扩展触觉内容创建提供了实用基础。
AI 中文摘要
触觉反馈被广泛用于增强虚拟现实（VR）环境的沉浸感，但设计覆盖多样交互条件的触觉刺激仍是重大的可扩展性挑战。数据驱动的触觉生成是颇具前景的方案，不过现有模型在波形表达性与推理响应性间存在固有权衡，且随训练数据规模和多样性增长，该权衡愈发关键。为解决此挑战，我们提出HaptoFlow，一种基于Flow Matching的振动触觉生成模型，专为VR中的交互式实时触觉渲染设计。Flow Matching学习将基础分布转换为目标数据分布的连续向量场，可高效表征复杂触觉数据分布，从而兼顾高质量生成与计算效率。我们基于材质标签和交互参数（划动速度与施加力）对HaptoFlow进行条件训练，并将其集成至VR系统。技术评估显示，HaptoFlow在波形复现精度和推理延迟上均优于所有基线方法；用户研究证实，系统延迟远低于视触觉延迟的感知阈值，且针对部分材质，感知触觉质量存在统计显著提升。这些发现为VR中可扩展的数据驱动触觉内容创建奠定了实用基础，并提供了延迟基准，可为未来实时触觉渲染系统的设计提供参考。项目页面：this https URL
英文摘要
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/.
Commentsaccepted to IEEE ISMAR2026 (conf. paper)
