Real-Time Neural Appearance Models
Tizian Zeltner, Fabrice Rousselle, Andrea Weidlich, Petrik Clarberg,, Jan Nov\'ak, Benedikt Bitterli, Alex Evans, Tom\'a\v{s} Davidovi\v{c}, Simon, Kallweit, Aaron Lefohn

TL;DR
This paper introduces a real-time neural appearance model that combines learned hierarchical textures and neural decoders, enabling high-quality rendering of complex materials in real-time applications like gaming.
Contribution
It presents a novel neural appearance model with hierarchical textures and priors, optimized for real-time rendering within a path tracer, surpassing traditional layered material performance.
Findings
Neural shaders are over an order of magnitude faster than traditional layered materials.
The system supports anisotropic sampling and level-of-detail rendering.
Efficient execution of neural decoders inside real-time path tracers is demonstrated.
Abstract
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 system level innovations. Our appearance model utilizes learned hierarchical textures that are interpreted using neural decoders, which produce reflectance values and importance-sampled directions. To best utilize the modeling capacity of the decoders, we equip the decoders with two graphics priors. The first prior -- transformation of directions into learned shading frames -- facilitates accurate reconstruction of mesoscale effects. The second prior -- a microfacet sampling distribution -- allows the neural decoder to perform importance sampling efficiently. The resulting appearance model supports anisotropic sampling and level-of-detail rendering, and allows baking deeply layered material graphs into a…
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Taxonomy
TopicsComputer Graphics and Visualization Techniques · 3D Shape Modeling and Analysis · Generative Adversarial Networks and Image Synthesis
