Game Level Generation from Gameplay Videos
نویسندگان
چکیده
We present an unsupervised approach to synthesize full video game levels from a set of gameplay videos. Our primary contribution is an unsupervised process to generate levels from a model trained on gameplay video. The model represents probabilistic relationships between shapes properties, and relates the relationships to stylistic variance within a domain. We utilize the classic platformer game Super Mario Bros. due to its highly-regarded level design. We evaluate the output in comparison to other data-driven level generation techniques via a user study and demonstrate its ability to produce novel output more stylistically similar to exemplar input.
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