# Texture Interpolation for Probing Visual Perception

arXiv: https://arxiv.org/abs/2006.03698 · HTML: https://mlsanity.com/paper/2006.03698/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 16 of 16)

- [The Intrinsic Dimension of Images and Its Impact on Learning](https://mlsanity.com/paper/2104.08894/) (2021)
- [Detecting Multimedia Generated by Large AI Models: A Survey](https://mlsanity.com/paper/2402.00045/) (2024)
- [Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality](https://mlsanity.com/paper/2307.02347/) (2023)
- [NeuroClips: Towards High-fidelity and Smooth fMRI-to-Video Reconstruction](https://mlsanity.com/paper/2410.19452/) (2024)
- [SGSST: Scaling Gaussian Splatting StyleTransfer](https://mlsanity.com/paper/2412.03371/) (2024)
- [On the relation between statistical learning and perceptual distances](https://mlsanity.com/paper/2106.04427/) (2021)
- [Wasserstein Distortion: Unifying Fidelity and Realism](https://mlsanity.com/paper/2310.03629/) (2023)
- [Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4](https://mlsanity.com/paper/2203.06649/) (2022)
- [Generalized Rectifier Wavelet Covariance Models For Texture Synthesis](https://mlsanity.com/paper/2203.07902/) (2022)
- [Wallpaper Texture Generation and Style Transfer Based on Multi-label Semantics](https://mlsanity.com/paper/2106.11482/) (2021)
- [Perceptual Scales Predicted by Fisher Information Metrics](https://mlsanity.com/paper/2310.11759/) (2023)
- [Perceptual misalignment of texture representations in convolutional neural networks](https://mlsanity.com/paper/2604.01341/) (2026)
- [Scaling Painting Style Transfer](https://mlsanity.com/paper/2212.13459/) (2022)
- [Multi-Texture Synthesis through Signal Responsive Neural Cellular Automata](https://mlsanity.com/paper/2407.05991/) (2024)
- [DiffStyle3D: Consistent 3D Gaussian Stylization via Attention Optimization](https://mlsanity.com/paper/2601.19717/) (2026)
- [Texture Interpolation as Texton Matching](https://mlsanity.com/paper/2610.18655/) (2026)

## Abstract (arXiv)

Texture synthesis models are important tools for understanding visual processing. In particular, statistical approaches based on neurally relevant features have been instrumental in understanding aspects of visual perception and of neural coding. New deep learning-based approaches further improve the quality of synthetic textures. Yet, it is still unclear why deep texture synthesis performs so well, and applications of this new framework to probe visual perception are scarce. Here, we show that distributions of deep convolutional neural network (CNN) activations of a texture are well described by elliptical distributions and therefore, following optimal transport theory, constraining their mean and covariance is sufficient to generate new texture samples. Then, we propose the natural geodesics (ie the shortest path between two points) arising with the optimal transport metric to interpolate between arbitrary textures. Compared to other CNN-based approaches, our interpolation method appears to match more closely the geometry of texture perception, and our mathematical framework is better suited to study its statistical nature. We apply our method by measuring the perceptual scale associated to the interpolation parameter in human observers, and the neural sensitivity of different areas of visual cortex in macaque monkeys.