44 citations · 141 across the 13 of their papers we have counts for
13 papers
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…
PSDR-Room: Single Photo to Scene using Differentiable Rendering
Kai Yan, Fujun Luan, MiloŠ HaŠAn +3
A 3D digital scene contains many components: lights, materials and geometries, interacting to reach the desired appearance. Staging such a scene is time-consuming and requires both…
PhotoMat: A Material Generator Learned from Single Flash Photos
Xilong Zhou, Miloš Hašan, Valentin Deschaintre +4
Authoring high-quality digital materials is key to realism in 3D rendering. Previous generative models for materials have been trained exclusively on synthetic data; such data is l…
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…
MatFormer: A Generative Model for Procedural Materials
Paul Guerrero, Miloš Hašan, Kalyan Sunkavalli +3
Procedural material graphs are a compact, parameteric, and resolution-independent representation that are a popular choice for material authoring. However, designing procedural mat…
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…
Differentiable Rendering of Neural SDFs through Reparameterization
Sai Praveen Bangaru, Michaël Gharbi, Tzu-Mao Li +7
We present a method to automatically compute correct gradients with respect to geometric scene parameters in neural SDF renderers. Recent physically-based differentiable rendering…
Physically-Based Editing of Indoor Scene Lighting from a Single Image
Zhengqin Li, Jia Shi, Sai Bi +6
We present a method to edit complex indoor lighting from a single image with its predicted depth and light source segmentation masks. This is an extremely challenging problem that…
Neural BRDFs: Representation and Operations
Jiahui Fan, Beibei Wang, Miloš Hašan +2
Bidirectional reflectance distribution functions (BRDFs) are pervasively used in computer graphics to produce realistic physically-based appearance. In recent years, several works…
SpongeCake: A Layered Microflake Surface Appearance Model
Beibei Wang, Wenhua Jin, Miloš Hašan +1
In this paper, we propose SpongeCake: a layered BSDF model where each layer is a volumetric scattering medium, defined using microflake or other phase functions. We omit any reflec…
OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets
Zhengqin Li, Ting-Wei Yu, Shen Sang +14
We propose a novel framework for creating large-scale photorealistic datasets of indoor scenes, with ground truth geometry, material, lighting and semantics. Our goal is to make th…