Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
[CVPR 2024] Official repository of "Material Palette: Extraction of Materials from a Single Real-world Image"
| Date | Stars |
|---|---|
| 2026-07-24 | 281 |
| 2026-07-25 | 281 |
| 2026-07-28 | 281 |
| 2026-07-30 | 281 |
| 2026-08-06 | 281 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
<div align='center'>
## Material Palette: Extraction of Materials from a Single Image (CVPR 2024)
<div>
<a href="https://wonjunior.github.io/">Ivan Lopes</a><sup>1</sup>
<a href="https://fabvio.github.io/">Fabio Pizzati</a><sup>2</sup>
<a href="https://team.inria.fr/rits/membres/raoul-de-charette/">Raoul de Charette</a><sup>1</sup>
<br>
<sup>1</sup> Inria,
<sup>2</sup> Oxford Uni.
</div>
<br>
[](https://astra-vision.github.io/MaterialPalette/)
[](https://github.com/astra-vision/MaterialPalette/releases/download/preprint/material_palette.pdf)
[](https://openaccess.thecvf.com/content/CVPR2024/html/Lopes_Material_Palette_Extraction_of_Materials_from_a_Single_Image_CVPR_2024_paper.html)
[](https://huggingface.co/datasets/ilopes/texsd)
[](https://github.com/astra-vision/MaterialPalette/stargazers)
<!--[](https://arxiv.org/abs/2311.17060)-->
<b>TL;DR,</b> Material Palette extracts a palette of PBR materials - <br>albedo, normals, and roughness - from a single real-world image.
</div>
https://github.com/astra-vision/MaterialPalette/assets/30524163/44e45e58-7c7d-49a3-8b6e-ec6b99cf9c62
<!--ts-->
* [Overview](#overview)
* [1. Installation](#1-installation)
* [2. Quick Start](#2-quick-start)
* [Generation](#-generation)
* [Complete Pipeline](#-complete-pipeline)
* [3. Project Structure](#3-project-structure)
* [4. (optional) Retraining](#4-optional-training)
* [Acknowledgments](#acknowledgments)
* [Licence](#license)
<!--te-->
<!--## 🚨 Todo
- 3D rendering script.-->
## Overview
This is the official repository of [**Material Palette**](https://astra-vision.github.io/MaterialPalette/). In a nutshell, the method works in three stages: first, concepts are extracted from an input image based on a user-provided mask; then, those concepts are used to generate texture images; finally, the generations are decomposed into SVBRDF maps (albedo, normals, and roughness). Visit our project page or consult our paper for more details!
**Content**: This repository allows the extraction of texture concepts from image and region mask sets. It also allows generation at different resolutions. Finally, it proposes a decomposition step thanks to our decomposition model, for which we share the training weights.
> [!TIP]
> We propose a ["Quick Start"](#2-quick-start) section: before diving straight into the full pipeline, we share four pretrained concepts ⚡ so you can go ahead and experiment with the texture generation step of the method: see ["§ Generation"](#-generation). Then you can try out the full method with your own image and masks = concept learning + generation + decomposition, see ["§ Complete Pipeline"](#-complete-pipeline).
## 1. Installation
1. Download the source code with git
```
git clone https://github.com/astra-vision/MaterialPalette.git
```
The repo can also be downloaded as a zip [here](https://github.com/astra-vision/MaterialPalette/archive/refs/heads/master.zip).
2. Create a conda environment with the dependencies.
```
conda env create --verbose -f deps.yml
```
This repo was tested with [**Python**](https://www.python.org/doc/versions/) 3.10.8, [**PyTorch**](https://pytorch.org/get-started/previous-versions/) 1.13, [**diffusers**](https://huggingface.co/docs/diffusers/installation) 0.19.3, [**peft**](https://huggingface.co/docs/peft/en/install) 0.5, and [**PyTorch Lightning*Excerpt of 20,448 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e80909b42bf80aa5, topic:computer-vision
matched fp:e80909b42bf80aa5, topic:stable-diffusion