Texture rendering has attracted significant attention as a means of creating realistic experiences in human-virtual object interactions. But in practical applications, many limited device conditions do not support the complete reproduction of spatial and temporal tactile stimuli. Different frequency components of designed vibrations can activate texture-related sensations owing to similar receptors. Therefore, we can utilize corresponding vibration signals to provide tactile feedback within the constraints of limited device environments. However, designing specific vibrations for numerous real-world materials is impractical. 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 introduced an optimization model based on Differential Subspace Search (DSS) and Generative Adversarial Network (GAN). With DSS, users can employ a one-dimensional slider to easily modify the high-dimensional latent space to ensure that the GAN can generate desired vibrations. We trained the generative model using an 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 indicated that our system could generate distinguishable samples that matched the target characteristics. Moreover, we established a correlation between subjects' ability to distinguish real samples and their ability to distinguish generated samples.

翻译：纹理渲染作为创造人类与虚拟物体交互真实体验的手段，已引起广泛关注。但在实际应用中，许多受限的设备条件无法完整复现空间与时间触觉刺激。由于相似受体的存在，设计振动的不同频率分量能够激活与纹理相关的感知。因此，我们可以在有限设备环境的约束下，利用相应的振动信号提供触觉反馈。然而，为大量现实材料设计特定振动是不切实际的。本研究提出了一种基于用户偏好的人在回路振动生成模型。为使用户能够轻松控制具有大参数空间的振动样本生成，我们引入了基于差分子空间搜索（DSS）与生成对抗网络（GAN）的优化模型。通过DSS，用户可使用一维滑块便捷地修改高维潜在空间，确保GAN能够生成期望的振动。我们使用公开的触觉振动数据集训练生成模型，并选择五类振动作为生成实验的目标样本。利用生成样本与真实样本开展了大规模用户实验。结果表明，我们的系统能够生成与目标特征匹配且可区分的样本。此外，我们建立了被试区分真实样本的能力与区分生成样本的能力之间的相关性。