activity
20182023
most citedGuided Fine-Tuning for Large-Scale Material Transfer

43 citations · 45 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV20232 cited

Floaters No More: Radiance Field Gradient Scaling for Improved Near-Camera Training

Julien Philip, Valentin Deschaintre

NeRF acquisition typically requires careful choice of near planes for the different cameras or suffers from background collapse, creating floating artifacts on the edges of the cap…

cs.CV2021

Deep Polarization Imaging for 3D shape and SVBRDF Acquisition

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. Unlike previous works that have exploited pola…

cs.GR2021

Generative Modelling of BRDF Textures from Flash Images

Philipp Henzler, Valentin Deschaintre, Niloy J. Mitra +1

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 mat…

cs.GR202043 cited

Guided Fine-Tuning for Large-Scale Material Transfer

Valentin Deschaintre, George Drettakis, Adrien Bousseau

We present a method to transfer the appearance of one or a few exemplar SVBRDFs to a target image representing similar materials. Our solution is extremely simple: we fine-tune a d…

cs.GR2019

Flexible SVBRDF Capture with a Multi-Image Deep Network

Valentin Deschaintre, Miika Aittala, Fredo Durand +2

Empowered by deep learning, recent methods for material capture can estimate a spatially-varying reflectance from a single photograph. Such lightweight capture is in stark contrast…

cs.GR2018

Single-Image SVBRDF Capture with a Rendering-Aware Deep Network

Valentin Deschaintre, Miika Aittala, Fredo Durand +2

Texture, highlights, and shading are some of many visual cues that allow humans to perceive material appearance in single pictures. Yet, recovering spatially-varying bi-directional…