18 citations · 21 across the 4 of their papers we have counts for
6 papers · 1 filter
Extracting Neural Materials from Multi-view 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…
VideoMatGen: PBR Materials through Joint Generative Modeling
Jon Hasselgren, Zheng Zeng, Milos Hasan +1
We present a method for generating physically-based materials for 3D shapes based on a video diffusion transformer architecture. Our method is conditioned on input geometry and a t…
UniRelight: Learning Joint Decomposition and Synthesis for Video Relighting
Kai He, Ruofan Liang, Jacob Munkberg +7
We address the challenge of relighting a single image or video, a task that demands precise scene intrinsic understanding and high-quality light transport synthesis. Existing end-t…
DiffusionRenderer: Neural Inverse and Forward Rendering with Video Diffusion Models
Ruofan Liang, Zan Gojcic, Huan Ling +8
Understanding and modeling lighting effects are fundamental tasks in computer vision and graphics. Classic physically-based rendering (PBR) accurately simulates the light transport…
Edify 3D: Scalable High-Quality 3D Asset Generation
NVIDIA, :, Maciej Bala +22
We introduce Edify 3D, an advanced solution designed for high-quality 3D asset generation. Our method first synthesizes RGB and surface normal images of the described object at mul…
Noise2Noise: Learning Image Restoration without Clean Data
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren +4
We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusio…