# Texture Mixer: A Network for Controllable Synthesis and Interpolation of Texture

arXiv: https://arxiv.org/abs/1901.03447 · HTML: https://mlsanity.com/paper/1901.03447/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2019.

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

- [Unleashing Transformers: Parallel Token Prediction with Discrete Absorbing Diffusion for Fast High-Resolution Image Generation from Vector-Quantized Codes](https://mlsanity.com/paper/2111.12701/) (2021)
- [Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis](https://mlsanity.com/paper/2105.14376/) (2021)
- [RepMix: Representation Mixing for Robust Attribution of Synthesized Images](https://mlsanity.com/paper/2207.02063/) (2022)
- [Dual Contrastive Loss and Attention for GANs](https://mlsanity.com/paper/2103.16748/) (2021)
- [SinIR: Efficient General Image Manipulation with Single Image Reconstruction](https://mlsanity.com/paper/2106.07140/) (2021)
- [Texture Generation with Neural Cellular Automata](https://mlsanity.com/paper/2105.07299/) (2021)
- [Generalized Rectifier Wavelet Covariance Models For Texture Synthesis](https://mlsanity.com/paper/2203.07902/) (2022)
- [Compositional Neural Textures](https://mlsanity.com/paper/2404.12509/) (2024)
- [BlendGAN: Learning and Blending the Internal Distributions of Single Images by Spatial Image-Identity Conditioning](https://mlsanity.com/paper/2212.01589/) (2022)
- [Content-aware Tile Generation using Exterior Boundary Inpainting](https://mlsanity.com/paper/2409.14184/) (2024)
- [Perceptual misalignment of texture representations in convolutional neural networks](https://mlsanity.com/paper/2604.01341/) (2026)
- [A geometrically aware auto-encoder for multi-texture synthesis](https://mlsanity.com/paper/2302.01616/) (2023)
- [Learning in a Single Domain for Non-Stationary Multi-Texture Synthesis](https://mlsanity.com/paper/2305.06200/) (2023)
- [Example-Based Feature Painting on Textures](https://mlsanity.com/paper/2511.01513/) (2025)
- [Texture Interpolation as Texton Matching](https://mlsanity.com/paper/2610.18655/) (2026)

## Abstract (arXiv)

This paper addresses the problem of interpolating visual textures. We formulate this problem by requiring (1) by-example controllability and (2) realistic and smooth interpolation among an arbitrary number of texture samples. To solve it we propose a neural network trained simultaneously on a reconstruction task and a generation task, which can project texture examples onto a latent space where they can be linearly interpolated and projected back onto the image domain, thus ensuring both intuitive control and realistic results. We show our method outperforms a number of baselines according to a comprehensive suite of metrics as well as a user study. We further show several applications based on our technique, which include texture brush, texture dissolve, and animal hybridization.