Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture
This addresses texture synthesis and interpolation for applications like digital art and graphics, but it is incremental as it builds on existing neural network methods.
The paper tackles the problem of interpolating visual textures with controllable synthesis, achieving realistic and smooth interpolation among multiple texture samples, as demonstrated by outperforming baselines in metrics and a user study.
This paper addresses the problem of interpolating visual textures. We formulate this problem by requiring (1) by-example controllability and (2) realistic and smooth interpolation among an arbitrary number of texture samples. To solve it we propose a neural network trained simultaneously on a reconstruction task and a generation task, which can project texture examples onto a latent space where they can be linearly interpolated and projected back onto the image domain, thus ensuring both intuitive control and realistic results. We show our method outperforms a number of baselines according to a comprehensive suite of metrics as well as a user study. We further show several applications based on our technique, which include texture brush, texture dissolve, and animal hybridization.