To create more realistic experiences in human-virtual object interactions, texture rendering has become a research hotspot in recent years. Different frequency components of designed vibrations can activate texture-related sensations due to similar receptors. However, designing specific vibrations for numerous real-world materials is impractical. Therefore, this study proposes a human-in-the-loop vibration generation model based on user preferences. To enable users to easily control the generation of vibration samples with large parameter spaces, we introduce an optimization model based on Differential Subspace Search (DSS) and Generative Adversarial Network (GAN). With DSS, users can use a one-dimensional slider to easily modify the high-dimensional latent space so that the GAN can generate desired vibrations. We trained the generative model using a open dataset of tactile vibration data and selected five types of vibrations as target samples for the generation experiment. Extensive user experiments were conducted using the generated and real samples. The results indicate that our system can generate distinguishable samples that match the target characteristics. Moreover, the results also reveal a correlation between subjects' ability to distinguish real samples and their ability to distinguish generated samples.

翻译：为提升人机交互中虚拟物体的真实感，纹理渲染技术近年来成为研究热点。由于相似感受器的作用，不同频率成分的振动可激发与纹理相关的感知。然而，为海量现实材料逐一设计特定振动模式并不现实。为此，本研究提出一种基于用户偏好的"人在回路"振动生成模型。为使用户能便捷调控高维参数空间的振动样本生成，我们引入基于差分子空间搜索与生成对抗网络的优化模型。通过差分子空间搜索技术，用户可利用一维滑块直观调整高维潜在空间，从而使生成对抗网络产生符合期望的振动模式。本研究采用公开触觉振动数据集训练生成模型，并选取五类典型振动作为生成实验的目标样本。通过大量用户实验对比生成样本与真实样本，结果表明：本系统能生成具有目标特征且可区分的振动样本；同时实验数据揭示了受试者对真实样本的区分能力与其对生成样本的区分能力之间存在相关性。