# 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
XRFeitoria

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## 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.