unrealcv: a bridge between Unreal Engine and AI frameworks for creating synthetic computer vision environments

What it solves

Computer vision researchers often need high-quality virtual environments to train and test their algorithms. UnrealCV provides a way to bridge the gap between a powerful game engine (Unreal Engine) and AI frameworks like PyTorch or TensorFlow, allowing researchers to generate synthetic data and interact with virtual worlds programmatically.

How it works

The project consists of a plugin for Unreal Engine (the Server) and a Python client. The plugin extends the engine with a set of commands that allow an external program to control the camera, manipulate objects, and capture images or optical flow. This communication allows a Python script to treat the virtual world as a source of data or a simulation environment.

Who it’s for

Computer vision researchers and developers who want to create synthetic training datasets or test AI agents in realistic 3D environments without needing deep expertise in game development.

Highlights

• Support for Unreal Engine 5.6.
• Ability to capture optical flow images.
• Integration with Python via a dedicated client library.
• Capability to call Blueprint functions directly from Python.
• Support for RPC communication on Linux for improved performance and reliability.