53 citations · 82 across the 13 of their papers we have counts for
12 papers · 1 filter
RGBX-Next: Towards Realistic Generative Rendering from G-Buffers
Zheng Zeng, Marco Salvi, Lifan Wu +9
Diffusion models have achieved impressive results in image, video, and streaming generation. However, compared to traditional 3D rendering, they still lack precise control over the…
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…
VideoNeuMat: Neural Material Extraction from Generative Video Models
Bowen Xue, Saeed Hadadan, Zheng Zeng +3
Creating photorealistic materials for 3D rendering requires exceptional artistic skill. Generative models for materials could help, but are currently limited by the lack of high-qu…
HiMat: DiT-based Ultra-High Resolution SVBRDF Generation
Zixiong Wang, Jian Yang, Yiwei Hu +2
Creating ultra-high-resolution spatially varying bidirectional reflectance functions (SVBRDFs) is critical for photorealistic 3D content creation, to faithfully represent fine-scal…
MaterialPicker: Multi-Modal DiT-Based Material Generation
Xiaohe Ma, Valentin Deschaintre, Miloš Hašan +4
High-quality material generation is key for virtual environment authoring and inverse rendering. We propose MaterialPicker, a multi-modal material generator leveraging a Diffusion…
Buffer Anytime: Zero-Shot Video Depth and Normal from Image Priors
Zhengfei Kuang, Tianyuan Zhang, Kai Zhang +7
We present Buffer Anytime, a framework for estimation of depth and normal maps (which we call geometric buffers) from video that eliminates the need for paired video--depth and vid…