{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/unrealcv-connecting-computer-vision-to-unreal","title":"UnrealCV: Connecting Computer Vision to Unreal Engine","arxiv_id":"1609.01326","date":"2016-09-05","proceeding":null,"authors":["Weichao Qiu","Alan Yuille"],"abstract":"Computer graphics can not only generate synthetic images and ground truth but\nit also offers the possibility of constructing virtual worlds in which: (i) an\nagent can perceive, navigate, and take actions guided by AI algorithms, (ii)\nproperties of the worlds can be modified (e.g., material and reflectance),\n(iii) physical simulations can be performed, and (iv) algorithms can be learnt\nand evaluated. But creating realistic virtual worlds is not easy. The game\nindustry, however, has spent a lot of effort creating 3D worlds, which a player\ncan interact with. So researchers can build on these resources to create\nvirtual worlds, provided we can access and modify the internal data structures\nof the games. To enable this we created an open-source plugin UnrealCV\n(http://unrealcv.github.io) for a popular game engine Unreal Engine 4 (UE4). We\nshow two applications: (i) a proof of concept image dataset, and (ii) linking\nCaffe with the virtual world to test deep network algorithms.","url_abs":"http://arxiv.org/abs/1609.01326v1","url_pdf":"http://arxiv.org/pdf/1609.01326v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"unrealcv-connecting-computer-vision-to-unreal","repo_url":"https://github.com/unrealcv/unrealcv","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"navigate","task_name":"Navigate"},{"task_slug":"physical-simulations","task_name":"Physical Simulations"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1609.01326","atlas_url":"https://app.syntology.ai/?focus=1609.01326","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1609.01326"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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