TY  - JOUR
A1  - Henzler, Philipp
A1  - Deschaintre, Valentin
A1  - Mitra, Niloy J
A1  - Ritschel, Tobias
JF  - ACM Transactions on Graphics
UR  - https://doi.org/10.1145/3478513.3480507
SN  - 0730-0301
IS  - 6
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
VL  - 40
KW  - Science & Technology
KW  -  Technology
KW  -  Computer Science
KW  -  Software Engineering
KW  -  Computer Science
KW  -  material capture
KW  -  appearance capture
KW  -  SVBRDF
KW  -  deep learning
KW  -  generative model
KW  -  unsupervised learning
KW  -  ILLUMINATION
KW  -  REFLECTANCE
PB  - ASSOC COMPUTING MACHINERY
N2  - 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. Project webpage: https://henzler.github.io/publication/neuralmaterial/.
ID  - discovery10159070
AV  - public
Y1  - 2021/12/01/
EP  - 13
TI  - Generative Modelling of BRDF Textures from Flash Images
ER  -