# Neural Texture Compression using Hypernetworks

arXiv: https://arxiv.org/abs/2606.26913 · HTML: https://mlsanity.com/paper/2606.26913/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 28 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Stylizing 3D Scene via Implicit Representation and HyperNetwork](https://mlsanity.com/paper/2105.13016/) (2021)
- [Hypernetwork functional image representation](https://mlsanity.com/paper/1902.10404/) (2019)
- [Image-GS: Content-Adaptive Image Representation via 2D Gaussians](https://mlsanity.com/paper/2407.01866/) (2024)
- [Peri-LN: Revisiting Normalization Layer in the Transformer Architecture](https://mlsanity.com/paper/2502.02732/) (2025)
- [Random-Access Neural Compression of Material Textures](https://mlsanity.com/paper/2305.17105/) (2023)
- [HyP-NeRF: Learning Improved NeRF Priors using a HyperNetwork](https://mlsanity.com/paper/2306.06093/) (2023)
- [HyperNST: Hyper-Networks for Neural Style Transfer](https://mlsanity.com/paper/2208.04807/) (2022)
- [Hypernetworks for Generalizable BRDF Representation](https://mlsanity.com/paper/2311.15783/) (2023)
- [Passing Multi-Channel Material Textures to a 3-Channel Loss](https://mlsanity.com/paper/2105.13012/) (2021)
- [Hardware Accelerated Neural Block Texture Compression with Cooperative Vectors](https://mlsanity.com/paper/2506.06040/) (2025)
- [Neural Graphics Texture Compression Supporting Random Access](https://mlsanity.com/paper/2407.00021/) (2024)

## Abstract (arXiv)

Recent work on neural texture compression has demonstrated that it is possible to learn small, per-material texture representations (composed of latent textures and a small Multi-Layer Perceptron decoder) that can be decoded in real-time during shading to reproduce the input to a physically based shading model. However, existing methods require performing gradient-descent optimization per material for a given MLP and latent configuration. In this work, we train a single hypernetwork that outputs both the latent features and the MLP's weights and biases. Though the solution space is high-dimensional, this approach produces results comparable in quality to the current reference neural texture compressors. We further extend this approach to infer multiple decoders at once or even produce decoders that learn super-resolution.