3 citations · 5 across the 5 of their papers we have counts for
7 papers
Texture Space Material Diffusion
Jacob Munkberg, Peter Kocsis, Jon Hasselgren
We present a method for generating high quality materials for 3D objects entirely in texture space. We finetune a video diffusion transformer for text-guided material generation, m…
Extracting Neural Materials from Images
Kim Youwang, Jon Hasselgren, Peter Kocsis +3
Neural materials can represent complex specular reflections and scattering effects in a compact, universal basis. However, acquiring and authoring such materials remains challengin…
Intrinsic Image Fusion for Multi-View 3D Material Reconstruction
Peter Kocsis, Lukas Höllein, Matthias Nießner
We introduce Intrinsic Image Fusion, a method that reconstructs high-quality physically based materials from multi-view images. Material reconstruction is highly underconstrained a…
IntrinsiX: High-Quality PBR Generation using Image Priors
Peter Kocsis, Lukas Höllein, Matthias Nießner
We introduce IntrinsiX, a novel method that generates high-quality intrinsic images from text description. In contrast to existing text-to-image models whose outputs contain baked-…
LightIt: Illumination Modeling and Control for Diffusion Models
Peter Kocsis, Julien Philip, Kalyan Sunkavalli +2
We introduce LightIt, a method for explicit illumination control for image generation. Recent generative methods lack lighting control, which is crucial to numerous artistic aspect…
Intrinsic Image Diffusion for Indoor Single-view Material Estimation
Peter Kocsis, Vincent Sitzmann, Matthias Nießner
We present Intrinsic Image Diffusion, a generative model for appearance decomposition of indoor scenes. Given a single input view, we sample multiple possible material explanations…
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes
Peter Kocsis, Peter Súkeník, Guillem Brasó +3
Convolutional neural networks were the standard for solving many computer vision tasks until recently, when Transformers of MLP-based architectures have started to show competitive…