283 citations · 613 across the 31 of their papers we have counts for
14 papers · 1 filter
RealMat: Realistic Materials with Diffusion and Reinforcement Learning
Xilong Zhou, Pedro Figueiredo, Miloš Hašan +4
Generative models for high-quality materials are particularly desirable to make 3D content authoring more accessible. However, the majority of material generation methods are train…
Fine-Grained Spatially Varying Material Selection in Images
Julia Guerrero-Viu, Michael Fischer, Iliyan Georgiev +4
Selection is the first step in many image editing processes, enabling faster and simpler modifications of all pixels sharing a common modality. In this work, we present a method fo…
IntrinsicEdit: Precise generative image manipulation in intrinsic space
Linjie Lyu, Valentin Deschaintre, Yannick Hold-Geoffroy +5
Generative diffusion models have advanced image editing with high-quality results and intuitive interfaces such as prompts and semantic drawing. However, these interfaces lack prec…
TexSliders: Diffusion-Based Texture Editing in CLIP Space
Julia Guerrero-Viu, Milos Hasan, Arthur Roullier +6
Generative models have enabled intuitive image creation and manipulation using natural language. In particular, diffusion models have recently shown remarkable results for natural…
The Visual Language of Fabrics
Valentin Deschaintre, Julia Guerrero-Viu, Diego Gutierrez +2
We introduce text2fabric, a novel dataset that links free-text descriptions to various fabric materials. The dataset comprises 15,000 natural language descriptions associated to 3,…
Generating Procedural Materials from Text or Image Prompts
Yiwei Hu, Paul Guerrero, Miloš Hašan +2
Node graph systems are used ubiquitously for material design in computer graphics. They allow the use of visual programming to achieve desired effects without writing code. As high…
Node Graph Optimization Using Differentiable Proxies
Yiwei Hu, Paul Guerrero, Miloš Hašan +2
Graph-based procedural materials are ubiquitous in content production industries. Procedural models allow the creation of photorealistic materials with parametric control for flexi…
Controlling Material Appearance by Examples
Yiwei Hu, Miloš Hašan, Paul Guerrero +2
Despite the ubiquitousness of materials maps in modern rendering pipelines, their editing and control remains a challenge. In this paper, we present an example-based material contr…
TileGen: Tileable, Controllable Material Generation and Capture
Xilong Zhou, Miloš Hašan, Valentin Deschaintre +3
Recent methods (e.g. MaterialGAN) have used unconditional GANs to generate per-pixel material maps, or as a prior to reconstruct materials from input photographs. These models can…
An Inverse Procedural Modeling Pipeline for SVBRDF Maps
Yiwei Hu, Chengan He, Valentin Deschaintre +2
Procedural modeling is now the de facto standard of material modeling in industry. Procedural models can be edited and are easily extended, unlike pixel-based representations of ca…
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…
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…
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…
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…