# xrfeitoria
**Repository Path**: OpenXRLab/xrfeitoria
## Basic Information
- **Project Name**: xrfeitoria
- **Description**: OpenXRLab Synthetic Data Rendering Toolbox
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-09-12
- **Last Updated**: 2026-01-08
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
[](https://xrfeitoria.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/openxrlab/xrfeitoria/actions)
[](https://pypi.org/project/xrfeitoria/)
[](https://www.apache.org/licenses/LICENSE-2.0)
## Introduction
XRFeitoria is a rendering toolbox for generating synthetic data photorealistic with ground-truth annotations.
It is a part of the [OpenXRLab](https://openxrlab.org.cn/) project.
https://github.com/openxrlab/xrfeitoria/assets/35397764/1e83bcd4-ae00-4c20-8188-3fe73f7c9c01
### Major Features
- Support rendering photorealistic images with ground-truth annotations.
- Support multiple engine backends, including [Unreal Engine](https://www.unrealengine.com/) and [Blender](https://www.blender.org/).
- Support assets/camera management, including import, place, export, and delete.
- Support a CLI tool to render images from a mesh file.
## Installation
```bash
pip install xrfeitoria
```
### Requirements
- `Python >= 3.8`
- (optional) `Unreal Engine >= 5.1`
- [x] Windows
- [x] Linux
- [ ] MacOS
- (optional) `Blender >= 3.0`
- [x] Windows
- [x] Linux
- [x] MacOS
## Get-Started
### CLI
```bash
xf-render --help
# render a mesh file
xf-render {mesh_file}
# for example
wget https://graphics.stanford.edu/~mdfisher/Data/Meshes/bunny.obj
xf-render bunny.obj
```
https://github.com/openxrlab/xrfeitoria/assets/35397764/430a7264-9337-4327-838d-08e9a354c277
https://github.com/openxrlab/xrfeitoria/assets/35397764/9c029eb7-a8be-4d11-890e-b2499ff22caa
### Documentation
The reference documentation is available on [readthedocs](https://xrfeitoria.readthedocs.io/en/latest/).
### Tutorials
There are several [tutorials](/tutorials/).
You can read them [here](https://xrfeitoria.readthedocs.io/en/latest/src/Tutorials.html).
### Sample codes
There are several [samples](/samples/).
Please follow the instructions [here](/samples/README.md).
### Use plugins under development
Details can be found [here](https://xrfeitoria.readthedocs.io/en/latest/faq.html#how-to-use-the-plugin-of-blender-unreal-under-development).
If you want to publish plugins of your own, you can use the following command:
```powershell
# install xrfeitoria first
cd xrfeitoria
pip install .
# build plugins for UE 5.1, UE 5.2, and UE 5.3 on Windows
python -m xrfeitoria.utils.publish_plugins build-unreal `
-u "C:/Program Files/Epic Games/UE_5.1/Engine/Binaries/Win64/UnrealEditor-Cmd.exe" `
-u "C:/Program Files/Epic Games/UE_5.2/Engine/Binaries/Win64/UnrealEditor-Cmd.exe" `
-u "C:/Program Files/Epic Games/UE_5.3/Engine/Binaries/Win64/UnrealEditor-Cmd.exe"
# build plugins for Blender
python -m xrfeitoria.utils.publish_plugins build-blender
```
### Frequently Asked Questions
Please refer to [FAQ](https://xrfeitoria.readthedocs.io/en/latest/faq.html).
## :rocket: Amazing Projects Using XRFeitoria
| Project | Teaser | Engine |
| :---: | :---: | :---: |
| [SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling](https://synbody.github.io/) |
| Unreal Engine / Blender |
| [Zolly: Zoom Focal Length Correctly for Perspective-Distorted Human Mesh Reconstruction](https://wenjiawang0312.github.io/projects/zolly/) |
| Blender |
| [SHERF: Generalizable Human NeRF from a Single Image](https://skhu101.github.io/SHERF/) |
| Blender |
| [MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyond](https://city-super.github.io/matrixcity/) |
| Unreal Engine |
| [HumanLiff: Layer-wise 3D Human Generation with Diffusion Model](https://skhu101.github.io/HumanLiff/) |
| Blender |
| [PrimDiffusion: Volumetric Primitives Diffusion for 3D Human Generation](https://frozenburning.github.io/projects/primdiffusion/) |
| Blender |
| [WHAC: World-grounded Humans and Cameras](https://wqyin.github.io/projects/WHAC/) |
| Unreal Engine |
| [SMPLest-X: Ultimate Scaling for Expressive Human Pose and Shape Estimation](https://caizhongang.com/projects/SMPLer-X/) |
| Blender |
## License
The license of our codebase is Apache-2.0. Note that this license only applies to code in our library, the dependencies of which are separate and individually licensed. We would like to pay tribute to open-source implementations to which we rely on. Please be aware that using the content of dependencies may affect the license of our codebase. Refer to [LICENSE](LICENSE) to view the full license.
## Citation
If you find this project useful in your research, please consider cite:
```bibtex
@misc{xrfeitoria,
title={OpenXRLab Synthetic Data Rendering Toolbox},
author={XRFeitoria Contributors},
howpublished = {\url{https://github.com/openxrlab/xrfeitoria}},
year={2023}
}
```
## Projects in OpenXRLab
- [XRPrimer](https://github.com/openxrlab/xrprimer): OpenXRLab foundational library for XR-related algorithms.
- [XRSLAM](https://github.com/openxrlab/xrslam): OpenXRLab Visual-inertial SLAM Toolbox and Benchmark.
- [XRSfM](https://github.com/openxrlab/xrsfm): OpenXRLab Structure-from-Motion Toolbox and Benchmark.
- [XRLocalization](https://github.com/openxrlab/xrlocalization): OpenXRLab Visual Localization Toolbox and Server.
- [XRMoCap](https://github.com/openxrlab/xrmocap): OpenXRLab Multi-view Motion Capture Toolbox and Benchmark.
- [XRMoGen](https://github.com/openxrlab/xrmogen): OpenXRLab Human Motion Generation Toolbox and Benchmark.
- [XRNeRF](https://github.com/openxrlab/xrnerf): OpenXRLab Neural Radiance Field (NeRF) Toolbox and Benchmark.
- [XRFeitoria](https://github.com/openxrlab/xrfeitoria): OpenXRLab Synthetic Data Rendering Toolbox.
- [XRViewer](https://github.com/openxrlab/xrviewer): OpenXRLab Data Visualization Toolbox.
- [XRTailor](https://github.com/openxrlab/xrtailor): OpenXRLab GPU Cloth Simulator.