Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry
Deep generative models can automatically create content of diverse types. However, there are no guarantees that such content will satisfy the criteria necessary to present it to end-users and be functional, e.g. the generated levels could be unsolvable or incoherent. In this paper we study this prob...
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creator | González-Duque, Miguel Palm, Rasmus Berg Hauberg, Søren Risi, Sebastian |
description | Deep generative models can automatically create content of diverse types.
However, there are no guarantees that such content will satisfy the criteria
necessary to present it to end-users and be functional, e.g. the generated
levels could be unsolvable or incoherent. In this paper we study this problem
from a geometric perspective, and provide a method for reliable interpolation
and random walks in the latent spaces of Categorical VAEs based on Riemannian
geometry. We test our method with "Super Mario Bros" and "The Legend of Zelda"
levels, and against simpler baselines inspired by current practice. Results
show that the geometry we propose is better able to interpolate and sample,
reliably staying closer to parts of the latent space that decode to playable
content. |
doi_str_mv | 10.48550/arxiv.2206.00106 |
format | Article |
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However, there are no guarantees that such content will satisfy the criteria
necessary to present it to end-users and be functional, e.g. the generated
levels could be unsolvable or incoherent. In this paper we study this problem
from a geometric perspective, and provide a method for reliable interpolation
and random walks in the latent spaces of Categorical VAEs based on Riemannian
geometry. We test our method with "Super Mario Bros" and "The Legend of Zelda"
levels, and against simpler baselines inspired by current practice. Results
show that the geometry we propose is better able to interpolate and sample,
reliably staying closer to parts of the latent space that decode to playable
content.</description><identifier>DOI: 10.48550/arxiv.2206.00106</identifier><language>eng</language><subject>Computer Science - Learning</subject><creationdate>2022-05</creationdate><rights>http://creativecommons.org/licenses/by-sa/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2206.00106$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2206.00106$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>González-Duque, Miguel</creatorcontrib><creatorcontrib>Palm, Rasmus Berg</creatorcontrib><creatorcontrib>Hauberg, Søren</creatorcontrib><creatorcontrib>Risi, Sebastian</creatorcontrib><title>Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry</title><description>Deep generative models can automatically create content of diverse types.
However, there are no guarantees that such content will satisfy the criteria
necessary to present it to end-users and be functional, e.g. the generated
levels could be unsolvable or incoherent. In this paper we study this problem
from a geometric perspective, and provide a method for reliable interpolation
and random walks in the latent spaces of Categorical VAEs based on Riemannian
geometry. We test our method with "Super Mario Bros" and "The Legend of Zelda"
levels, and against simpler baselines inspired by current practice. Results
show that the geometry we propose is better able to interpolate and sample,
reliably staying closer to parts of the latent space that decode to playable
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However, there are no guarantees that such content will satisfy the criteria
necessary to present it to end-users and be functional, e.g. the generated
levels could be unsolvable or incoherent. In this paper we study this problem
from a geometric perspective, and provide a method for reliable interpolation
and random walks in the latent spaces of Categorical VAEs based on Riemannian
geometry. We test our method with "Super Mario Bros" and "The Legend of Zelda"
levels, and against simpler baselines inspired by current practice. Results
show that the geometry we propose is better able to interpolate and sample,
reliably staying closer to parts of the latent space that decode to playable
content.</abstract><doi>10.48550/arxiv.2206.00106</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Learning |
title | Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry |
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