980 citations · 1.1k across the 14 of their papers we have counts for
16 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…
Toward Richer Material Generation via Procedural Data Enhancement
Yunchen Yu, Jacob Munkberg, Jon Hasselgren +3
Generative models for material creation are fundamentally limited by the quality and expressivity of available training data. Simple physically based rendering (PBR) materials, whi…
AlbedoEdit: Unified Instance-Level Video Editing with Albedo Guidance
Xilong Zhou, Bao-Huy Nguyen, Zheng Zeng +6
Video generative models have achieved remarkable progress in synthesizing photorealistic video sequences. However, enabling broader and more creative downstream applications requir…
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…