no code implementations • 23 Feb 2022 • Shouchang Guo, Valentin Deschaintre, Douglas Noll, Arthur Roullier
We present a novel U-Attention vision Transformer for universal texture synthesis.
no code implementations • CVPR 2021 • Valentin Deschaintre, Yiming Lin, Abhijeet Ghosh
We present a novel method for efficient acquisition of shape and spatially varying reflectance of 3D objects using polarization cues.
no code implementations • 23 Feb 2021 • Philipp Henzler, Valentin Deschaintre, Niloy J. Mitra, Tobias Ritschel
We learn a latent space for easy capture, consistent interpolation, and efficient reproduction of visual material appearance.
1 code implementation • 6 Jul 2020 • Valentin Deschaintre, George Drettakis, Adrien Bousseau
Our solution is extremely simple: we fine-tune a deep appearance-capture network on the provided exemplars, such that it learns to extract similar SVBRDF values from the target image.
1 code implementation • 27 Jun 2019 • Valentin Deschaintre, Miika Aittala, Fredo Durand, George Drettakis, Adrien Bousseau
Empowered by deep learning, recent methods for material capture can estimate a spatially-varying reflectance from a single photograph.
Graphics I.3
1 code implementation • 23 Oct 2018 • Valentin Deschaintre, Miika Aittala, Fredo Durand, George Drettakis, Adrien Bousseau
Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures.
Graphics I.3