Introduction

XRFeitoria is a rendering toolbox for generating synthetic data photorealistic with ground-truth annotations. It is a part of the OpenXRLab project.

XRFeitoria-Intro.mp4

Major Features

• Support rendering photorealistic images with ground-truth annotations.
• Support multiple engine backends, including Unreal Engine and Blender.
• Support assets/camera management, including import, place, export, and delete.
• Support a CLI tool to render images from a mesh file.

Installation

pip install xrfeitoria

Requirements

• Python >= 3.8
• (optional) Unreal Engine >= 5.1
  • Windows
  • Linux
  • MacOS
• (optional) Blender >= 3.0
  • Windows
  • Linux
  • MacOS

Get-Started

CLI

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

CLI-simple.mp4

CLI-complex.mp4

Documentation

The reference documentation is available on readthedocs.

Tutorials

There are several tutorials. You can read them here.

Sample codes

There are several samples. Please follow the instructions here.

Use plugins under development

Details can be found here.

If you want to publish plugins of your own, you can use the following command:

# 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.

🚀 Amazing Projects Using XRFeitoria

Project	Teaser	Engine
SynBody: Synthetic Dataset with Layered Human Models for 3D Human Perception and Modeling		Unreal Engine / Blender
Zolly: Zoom Focal Length Correctly for Perspective-Distorted Human Mesh Reconstruction		Blender
SHERF: Generalizable Human NeRF from a Single Image		Blender
MatrixCity: A Large-scale City Dataset for City-scale Neural Rendering and Beyond		Unreal Engine
HumanLiff: Layer-wise 3D Human Generation with Diffusion Model		Blender
PrimDiffusion: Volumetric Primitives Diffusion for 3D Human Generation		Blender
WHAC: World-grounded Humans and Cameras		Unreal Engine

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 to view the full license.

Citation

If you find this project useful in your research, please consider cite:

@misc{xrfeitoria,
    title={OpenXRLab Synthetic Data Rendering Toolbox},
    author={XRFeitoria Contributors},
    howpublished = {\url{https://github.com/openxrlab/xrfeitoria}},
    year={2023}
}

Projects in OpenXRLab

• XRPrimer: OpenXRLab foundational library for XR-related algorithms.
• XRSLAM: OpenXRLab Visual-inertial SLAM Toolbox and Benchmark.
• XRSfM: OpenXRLab Structure-from-Motion Toolbox and Benchmark.
• XRLocalization: OpenXRLab Visual Localization Toolbox and Server.
• XRMoCap: OpenXRLab Multi-view Motion Capture Toolbox and Benchmark.
• XRMoGen: OpenXRLab Human Motion Generation Toolbox and Benchmark.
• XRNeRF: OpenXRLab Neural Radiance Field (NeRF) Toolbox and Benchmark.
• XRFeitoria: OpenXRLab Synthetic Data Rendering Toolbox.