[1912.04158v1] Learning a Neural 3D Texture Space from 2D Exemplars
Abstract: We propose a generative model of 2D and 3D natural textures with diversity,
visual fidelity and at high computational efficiency. This is enabled by a
family of methods that extend ideas from classic stochastic procedural
texturing (Perlin noise) to learned, deep, non-linearities. The key idea is a
hard-coded, tunable and differentiable step that feeds multiple transformed
random 2D or 3D fields into an MLP that can be sampled over infinite domains.
Our model encodes all exemplars from a diverse set of textures without a need
to be re-trained for each exemplar. Applications include texture interpolation,
and learning 3D textures from 2D exemplars.