  Reconstructing materials in the real world has always been a difficult
problem in computer graphics. Accurately reconstructing the material in the
real world is critical in the field of realistic rendering. Traditionally,
materials in computer graphics are mapped by an artist, then mapped onto a
geometric model by coordinate transformation, and finally rendered with a
rendering engine to get realistic materials. For opaque objects, the industry
commonly uses physical-based bidirectional reflectance distribution function
(BRDF) rendering models for material modeling. The commonly used physical-based
rendering models are Cook-Torrance BRDF, Disney BRDF. In this paper, we use the
Cook-Torrance model to reconstruct the materials. The SVBRDF material
parameters include Normal, Diffuse, Specular and Roughness. This paper presents
a Diffuse map guiding material estimation method based on the Generative
Adversarial Network(GAN). This method can predict plausible SVBRDF maps with
global features using only a few pictures taken by the mobile phone. The main
contributions of this paper are: 1) We preprocess a small number of input
pictures to produce a large number of non-repeating pictures for training to
reduce over-fitting. 2) We use a novel method to directly obtain the guessed
diffuse map with global characteristics, which provides more prior information
for the training process. 3) We improve the network architecture of the
generator so that it can generate fine details of normal maps and reduce the
possibility to generate over-flat normal maps. The method used in this paper
can obtain prior knowledge without using dataset training, which greatly
reduces the difficulty of material reconstruction and saves a lot of time to
generate and calibrate datasets.
