University of Southern California

Shihan Lu

Heather Culbertson

Mianlun Zheng

Matthew Fontaine

Human interactive texture generation and search systems and methods are described. A deep convolutional generative adversarial network is used for mapping information in a latent space into texture models. An interactive evolutionary computation algorithm for searching a texture through an evolving latent space driven by human preference is also described. Advantages of a generative model and an evolutionary computation are combined to realize a controllable and bounded texture tuning process under the guidance of human preferences. Additionally, a fully haptic user interface is described, which can be used to evaluate the systems and methods in terms of their efficiency and accuracy of searching and generating new virtual textures that are closely representative of given real textures.

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Shihan Lu