IEEE Trans Haptics. 2022 Jul-Sep;15(3):508-520. doi: 10.1109/TOH.2022.3173935. Epub 2022 Sep 27.
Data-driven texture modeling and rendering has pushed the limit of realism in haptics. However, the lack of haptic texture databases, difficulties of model interpolation and expansion, and the complexity of real textures prevent data-driven methods from capturing a large variety of textures and from customizing models to suit specific output hardware or user needs. This work proposes an interactive texture generation and search framework driven by user input. We design a GAN-based texture model generator, which can create a wide range of texture models using Auto-Regressive processes. Our interactive texture search method, which we call "preference-driven," follows an evolutionary strategy given guidance from user's preferred feedback within a set of generated texture models. We implemented this framework on a 3D haptic device and conducted a two-phase user study to evaluate the efficiency and accuracy of our method for previously unmodeled textures. The results showed that by comparing the feel of real and generated virtual textures, users can follow an evolutionary process to efficiently find a virtual texture model that matched or exceeded the realism of a data-driven model. Furthermore, for 4 out of 5 real textures, ≥ 80% of the preference-driven models from participants were rated comparable to the data-driven models.
数据驱动的纹理建模和渲染技术在触觉领域已经达到了极高的逼真度。然而，触觉纹理数据库的缺乏、模型插值和扩展的困难，以及真实纹理的复杂性，都阻碍了数据驱动方法捕捉大量不同纹理的能力，也无法使模型定制以适应特定的输出硬件或用户需求。本工作提出了一种基于用户输入的交互式纹理生成和搜索框架。我们设计了一个基于 GAN 的纹理模型生成器，它可以使用自回归过程创建广泛的纹理模型。我们的交互式纹理搜索方法，我们称之为“偏好驱动”，它遵循一种进化策略，根据用户在一组生成的纹理模型中的偏好反馈来提供指导。我们在一个 3D 触觉设备上实现了这个框架，并进行了两阶段的用户研究，以评估我们的方法对以前未建模纹理的效率和准确性。结果表明，通过比较真实和生成的虚拟纹理的感觉，用户可以通过进化过程，有效地找到一个虚拟纹理模型，其逼真度与数据驱动模型相匹配或超过数据驱动模型。此外，对于 5 种真实纹理中的 4 种，参与者的偏好驱动模型中有≥80%的模型被评为与数据驱动模型相当。
