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UnrealCV: Connecting Computer Vision to Unreal Engine

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arxiv 1609.01326 v1 pith:V6VCSSDI submitted 2016-09-05 cs.CV

classification cs.CV
keywords worldsvirtualalgorithmsengineunrealcvcomputercreatinggame
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PhysEditWorld: A Large-Scale Dataset Toward Physics-Editable World Models

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    PhysEditWorld supplies 12 UE5 scenes, 60+ million frames, and explicit gravity labels via a replay paradigm to support gravity-faithful and physically editable world models.

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