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.

中文翻译：

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从 2D 示例中学习神经 3D 纹理空间

我们提出了一种具有多样性、视觉保真度和高计算效率的 2D 和 3D 自然纹理的生成模型。这是通过一系列方法实现的，这些方法将思想从经典的随机程序纹理（柏林噪声）扩展到学习的、深度的、非线性的。关键思想是一个硬编码、可调和可微分的步骤，它将多个变换后的随机 2D 或 3D 场馈送到可以在无限域上采样的 MLP。我们的模型对来自不同纹理集的所有样本进行编码，而无需为每个样本重新训练。应用包括纹理插值和从 2D 样本中学习 3D 纹理。