53 citations · 89 across the 19 of their papers we have counts for
19 papers
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…
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…
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…
IntrinsicEdit: Precise generative image manipulation in intrinsic space
Linjie Lyu, Valentin Deschaintre, Yannick Hold-Geoffroy +5
Generative diffusion models have advanced image editing with high-quality results and intuitive interfaces such as prompts and semantic drawing. However, these interfaces lack prec…
Position-Normal Manifold for Efficient Glint Rendering on High-Resolution Normal Maps
Liwen Wu, Fujun Luan, Miloš Hašan +1
Detailed microstructures on specular objects often exhibit intriguing glinty patterns under high-frequency lighting, which is challenging to render using a conventional normal-mapp…
Generating 360° Video is What You Need For a 3D Scene
Zhaoyang Zhang, Yannick Hold-Geoffroy, Miloš Hašan +4
Generating 3D scenes is still a challenging task due to the lack of readily available scene data. Most existing methods only produce partial scenes and provide limited navigational…
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…
RelitLRM: Generative Relightable Radiance for Large Reconstruction Models
Tianyuan Zhang, Zhengfei Kuang, Haian Jin +9
We propose RelitLRM, a Large Reconstruction Model (LRM) for generating high-quality Gaussian splatting representations of 3D objects under novel illuminations from sparse (4-8) pos…
Neural Product Importance Sampling via Warp Composition
Joey Litalien, Miloš Hašan, Fujun Luan +2
Achieving high efficiency in modern photorealistic rendering hinges on using Monte Carlo sampling distributions that closely approximate the illumination integral estimated for eve…
RNG: Relightable Neural Gaussians
Jiahui Fan, Fujun Luan, Jian Yang +2
3D Gaussian Splatting (3DGS) has shown impressive results for the novel view synthesis task, where lighting is assumed to be fixed. However, creating relightable 3D assets, especia…
PBIR-NIE: Glossy Object Capture under Non-Distant Lighting
Guangyan Cai, Fujun Luan, Miloš Hašan +5
Glossy objects present a significant challenge for 3D reconstruction from multi-view input images under natural lighting. In this paper, we introduce PBIR-NIE, an inverse rendering…
Uncertainty for SVBRDF Acquisition using Frequency Analysis
Ruben Wiersma, Julien Philip, Miloš Hašan +4
This paper aims to quantify uncertainty for SVBRDF acquisition in multi-view captures. Under uncontrolled illumination and unstructured viewpoints, there is no guarantee that the o…
TexSliders: Diffusion-Based Texture Editing in CLIP Space
Julia Guerrero-Viu, Milos Hasan, Arthur Roullier +6
Generative models have enabled intuitive image creation and manipulation using natural language. In particular, diffusion models have recently shown remarkable results for natural…
RGBX: Image decomposition and synthesis using material- and lighting-aware diffusion models
Zheng Zeng, Valentin Deschaintre, Iliyan Georgiev +5
The three areas of realistic forward rendering, per-pixel inverse rendering, and generative image synthesis may seem like separate and unrelated sub-fields of graphics and vision.…
Rendering Participating Media Using Path Graphs
Becky Hu, Xi Deng, Fujun Luan +2
Rendering volumetric scattering media, including clouds, fog, smoke, and other complex materials, is crucial for realism in computer graphics. Traditional path tracing, while unbia…
RNA: Relightable Neural Assets
Krishna Mullia, Fujun Luan, Xin Sun +1
High-fidelity 3D assets with materials composed of fibers (including hair), complex layered material shaders, or fine scattering geometry are ubiquitous in high-end realistic rende…