132 citations · 182 across the 6 of their papers we have counts for
8 papers
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…
Generative Detail Enhancement for Physically Based Materials
Saeed Hadadan, Benedikt Bitterli, Tizian Zeltner +6
We present a tool for enhancing the detail of physically based materials using an off-the-shelf diffusion model and inverse rendering. Our goal is to enhance the visual fidelity of…
Radiance Surfaces: Optimizing Surface Representations with a 5D Radiance Field Loss
Ziyi Zhang, Nicolas Roussel, Thomas Müller +4
We present a fast and simple technique to convert images into a radiance surface-based scene representation. Building on existing radiance volume reconstruction algorithms, we intr…
Inverse Global Illumination using a Neural Radiometric Prior
Saeed Hadadan, Geng Lin, Jan Novák +2
Inverse rendering methods that account for global illumination are becoming more popular, but current methods require evaluating and automatically differentiating millions of path…
Real-Time Neural Appearance Models
Tizian Zeltner, Fabrice Rousselle, Andrea Weidlich +7
We present a complete system for real-time rendering of scenes with complex appearance previously reserved for offline use. This is achieved with a combination of algorithmic and s…
Real-time Neural Radiance Caching for Path Tracing
Thomas Müller, Fabrice Rousselle, Jan Novák +1
We present a real-time neural radiance caching method for path-traced global illumination. Our system is designed to handle fully dynamic scenes, and makes no assumptions about the…
Neural Control Variates
Thomas Müller, Fabrice Rousselle, Jan Novák +1
We propose neural control variates (NCV) for unbiased variance reduction in parametric Monte Carlo integration. So far, the core challenge of applying the method of control variate…
Neural Importance Sampling
Thomas Müller, Brian McWilliams, Fabrice Rousselle +2
We propose to use deep neural networks for generating samples in Monte Carlo integration. Our work is based on non-linear independent components estimation (NICE), which we extend…