UnrealCV: Connecting Computer Vision to Unreal Engine
This tool addresses the problem of accessing and modifying game data for researchers in AI and computer vision, though it is incremental by building on existing game industry resources.
The paper tackles the challenge of creating realistic virtual worlds for AI research by developing UnrealCV, an open-source plugin for Unreal Engine 4 that connects computer vision to game engines, enabling applications such as generating synthetic image datasets and testing deep network algorithms.
Computer graphics can not only generate synthetic images and ground truth but it also offers the possibility of constructing virtual worlds in which: (i) an agent can perceive, navigate, and take actions guided by AI algorithms, (ii) properties of the worlds can be modified (e.g., material and reflectance), (iii) physical simulations can be performed, and (iv) algorithms can be learnt and evaluated. But creating realistic virtual worlds is not easy. The game industry, however, has spent a lot of effort creating 3D worlds, which a player can interact with. So researchers can build on these resources to create virtual worlds, provided we can access and modify the internal data structures of the games. To enable this we created an open-source plugin UnrealCV (http://unrealcv.github.io) for a popular game engine Unreal Engine 4 (UE4). We show two applications: (i) a proof of concept image dataset, and (ii) linking Caffe with the virtual world to test deep network algorithms.