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arXiv · 1901.03447

Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture

Ning Yu·Connelly Barnes·Eli Shechtman·Sohrab Amirghodsi·Michal Lukac
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Abstract

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.

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Ning Yu, Connelly Barnes, Eli Shechtman, Sohrab Amirghodsi, Michal Lukac. 2019-01-11. Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture. https://arxiv.org/abs/1901.03447

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