User-Controllable Multi-Texture Synthesis with Generative Adversarial Networks
This work addresses texture synthesis for applications like computer graphics and design, offering incremental improvements in user control and 3D texture generation.
The paper tackles the problem of multi-texture synthesis by introducing a user-controllable GAN model that allows explicit texture specification, achieving improved performance in generating 3D textures compared to existing baselines.
We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism. The user control ability allows to explicitly specify the texture which should be generated by the model. This property follows from using an encoder part which learns a latent representation for each texture from the dataset. To ensure a dataset coverage, we use an adversarial loss function that penalizes for incorrect reproductions of a given texture. In experiments, we show that our model can learn descriptive texture manifolds for large datasets and from raw data such as a collection of high-resolution photos. Moreover, we apply our method to produce 3D textures and show that it outperforms existing baselines.