# NeuMIP: Multi-Resolution Neural Materials

arXiv: https://arxiv.org/abs/2104.02789 · HTML: https://mlsanity.com/paper/2104.02789/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 6 of 6)

- [Deferred Neural Rendering: Image Synthesis using Neural Textures](https://mlsanity.com/paper/1904.12356/) (2019)
- [Neural Importance Sampling](https://mlsanity.com/paper/1808.03856/) (2018)
- [Deep Shading: Convolutional Neural Networks for Screen-Space Shading](https://mlsanity.com/paper/1603.06078/) (2016)
- [Neural Control Variates](https://mlsanity.com/paper/2006.01524/) (2020)
- [Deep Reflectance Maps](https://mlsanity.com/paper/1511.04384/) (2015)
- [Deep Appearance Maps](https://mlsanity.com/paper/1804.00863/) (2018)

## Built on by (most cited first, 15 of 15)

- [WIRE: Wavelet Implicit Neural Representations](https://mlsanity.com/paper/2301.05187/) (2023)
- [MINER: Multiscale Implicit Neural Representations](https://mlsanity.com/paper/2202.03532/) (2022)
- [PhotoMat: A Material Generator Learned from Single Flash Photos](https://mlsanity.com/paper/2305.12296/) (2023)
- [Neural Gaussian Scale-Space Fields](https://mlsanity.com/paper/2405.20980/) (2024)
- [Neural Fields with Hard Constraints of Arbitrary Differential Order](https://mlsanity.com/paper/2306.08943/) (2023)
- [A Hierarchical Architecture for Neural Materials](https://mlsanity.com/paper/2307.10135/) (2023)
- [Learning Images Across Scales Using Adversarial Training](https://mlsanity.com/paper/2406.08924/) (2024)
- [Neural Product Importance Sampling via Warp Composition](https://mlsanity.com/paper/2409.18974/) (2024)
- [Neural BRDF Importance Sampling by Reparameterization](https://mlsanity.com/paper/2505.08998/) (2025)
- [Efficient Scene Appearance Aggregation for Level-of-Detail Rendering](https://mlsanity.com/paper/2409.03761/) (2024)
- [On Neural BRDFs: A Thorough Comparison of State-of-the-Art Approaches](https://mlsanity.com/paper/2502.15480/) (2025)
- [VideoNeuMat: Neural Material Extraction from Generative Video Models](https://mlsanity.com/paper/2602.07272/) (2026)
- [Acquisition of Spatially-Varying Reflectance and Surface Normals via Polarized Reflectance Fields](https://mlsanity.com/paper/2412.09772/) (2024)
- [8DNA: 8D Neural Asset Light Transport by Distribution Learning](https://mlsanity.com/paper/2604.25129/) (2026)
- [Real-time Neural Woven Fabric Rendering](https://mlsanity.com/paper/2406.17782/) (2024)

## Abstract (arXiv)

We propose NeuMIP, a neural method for representing and rendering a variety of material appearances at different scales. Classical prefiltering (mipmapping) methods work well on simple material properties such as diffuse color, but fail to generalize to normals, self-shadowing, fibers or more complex microstructures and reflectances. In this work, we generalize traditional mipmap pyramids to pyramids of neural textures, combined with a fully connected network. We also introduce neural offsets, a novel method which allows rendering materials with intricate parallax effects without any tessellation. This generalizes classical parallax mapping, but is trained without supervision by any explicit heightfield. Neural materials within our system support a 7-dimensional query, including position, incoming and outgoing direction, and the desired filter kernel size. The materials have small storage (on the order of standard mipmapping except with more texture channels), and can be integrated within common Monte-Carlo path tracing systems. We demonstrate our method on a variety of materials, resulting in complex appearance across levels of detail, with accurate parallax, self-shadowing, and other effects.