eprintid: 10159070
rev_number: 6
eprint_status: archive
userid: 699
dir: disk0/10/15/90/70
datestamp: 2022-11-14 14:53:29
lastmod: 2022-11-14 14:53:29
status_changed: 2022-11-14 14:53:29
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Henzler, Philipp
creators_name: Deschaintre, Valentin
creators_name: Mitra, Niloy J
creators_name: Ritschel, Tobias
title: Generative Modelling of BRDF Textures from Flash Images
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
keywords: Science & Technology, Technology, Computer Science, Software Engineering, Computer Science, material capture, appearance capture, SVBRDF, deep learning, generative model, unsupervised learning, ILLUMINATION, REFLECTANCE
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: 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/.
date: 2021-12-01
date_type: published
publisher: ASSOC COMPUTING MACHINERY
official_url: https://doi.org/10.1145/3478513.3480507
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1914734
doi: 10.1145/3478513.3480507
lyricists_name: Mitra, Niloy
lyricists_id: NMITR19
actors_name: Mitra, Niloy
actors_id: NMITR19
actors_role: owner
funding_acknowledgements: [ERC]; [Google AR/VR Research Award]; EP/N006259/1 [EPSRC]
full_text_status: public
publication: ACM Transactions on Graphics
volume: 40
number: 6
article_number: 284
pages: 13
issn: 0730-0301
citation:        Henzler, Philipp;    Deschaintre, Valentin;    Mitra, Niloy J;    Ritschel, Tobias;      (2021)    Generative Modelling of BRDF Textures from Flash Images.                   ACM Transactions on Graphics , 40  (6)    , Article 284.  10.1145/3478513.3480507 <https://doi.org/10.1145/3478513.3480507>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10159070/1/brdf.pdf