We learn a latent space for easy capture, consistent interpolation, and efficient reproduction of visual material appearance. When users provide a photo of a stationary natural material captured under flashlight illumination, first it is converted into a latent material code. Then, in the second step, conditioned on the material code, our method produces an infinite and diverse spatial field of BRDF model parameters (diffuse albedo, normals, roughness, specular albedo) that subsequently allows rendering in complex scenes and illuminations, matching the appearance of the input photograph. Technically, we jointly embed all flash images into a latent space using a convolutional encoder, and -- conditioned on these latent codes -- convert random spatial fields into fields of BRDF parameters using a convolutional neural network (CNN). We condition these BRDF parameters to match the visual characteristics (statistics and spectra of visual features) of the input under matching light. A user study compares our approach favorably to previous work, even those with access to BRDF supervision.

翻译：我们学习了易于捕捉、一致的内插和有效复制视觉材料外观的潜在空间。 当用户提供在闪光光下捕捉的固定自然材料的照片时, 首先它被转换成潜在的材料代码。 然后,在第二步,以材料代码为条件,我们的方法产生了一个无限和多样的BRDF模型参数空间领域( diffuse albedo, normals, roughness, speales albedo),这些参数随后允许在复杂的场景和光谱中显示,与输入照片的外观相匹配。技术上,我们用一个革命编码将所有闪光图像联合嵌入一个潜在空间,并且 -- -- 以这些潜在编码为条件 -- -- 将随机空间域转换成使用动态神经网络(CNN)的BRDF参数。我们将这些BDF参数设置在匹配光下输入的视觉特征(统计和视觉特征的光谱)的参数。用户研究将我们的方法与以前的工作相比是可取的, 甚至是有BDF监督功能的。