  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.
