Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool
Sketches are abstract representations of visual perception and visuospatial construction. In this work, we proposed a new framework, Generative Adversarial Networks with Conditional Neural Movement Primitives (GAN-CNMP), that incorporates a novel adversarial loss on CNMP to increase sketch smoothnes...
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| creator | Ada, Suzan Ece Seker, M. Yunus |
| description | Sketches are abstract representations of visual perception and visuospatial
construction. In this work, we proposed a new framework, Generative Adversarial
Networks with Conditional Neural Movement Primitives (GAN-CNMP), that
incorporates a novel adversarial loss on CNMP to increase sketch smoothness and
consistency. Through the experiments, we show that our model can be trained
with few unlabeled samples, can construct distributions automatically in the
latent space, and produces better results than the base model in terms of shape
consistency and smoothness. |
| doi_str_mv | 10.48550/arxiv.2111.14934 |
| format | Article |
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construction. In this work, we proposed a new framework, Generative Adversarial
Networks with Conditional Neural Movement Primitives (GAN-CNMP), that
incorporates a novel adversarial loss on CNMP to increase sketch smoothness and
consistency. Through the experiments, we show that our model can be trained
with few unlabeled samples, can construct distributions automatically in the
latent space, and produces better results than the base model in terms of shape
consistency and smoothness.</description><identifier>DOI: 10.48550/arxiv.2111.14934</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Graphics ; Computer Science - Learning ; Computer Science - Neural and Evolutionary Computing</subject><creationdate>2021-11</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</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/2111.14934$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2111.14934$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Ada, Suzan Ece</creatorcontrib><creatorcontrib>Seker, M. Yunus</creatorcontrib><title>Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool</title><description>Sketches are abstract representations of visual perception and visuospatial
construction. In this work, we proposed a new framework, Generative Adversarial
Networks with Conditional Neural Movement Primitives (GAN-CNMP), that
incorporates a novel adversarial loss on CNMP to increase sketch smoothness and
consistency. Through the experiments, we show that our model can be trained
with few unlabeled samples, can construct distributions automatically in the
latent space, and produces better results than the base model in terms of shape
consistency and smoothness.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Computer Vision and Pattern Recognition</subject><subject>Computer Science - Graphics</subject><subject>Computer Science - Learning</subject><subject>Computer Science - Neural and Evolutionary Computing</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNpNkM1OhDAUhbtxYUYfwJV9AbClP8CSoI6TjD8L9uRC25lGpjWlgr69DLpwc8_i5nw55yB0Q0nKCyHIHYQvO6UZpTSlvGT8Ek1b7XSAaCeNKzXpMEKwMOAXHWcf3kc823jEtXfKRuvd-vkMizz7SZ-0i_gt2JM9-0dsfMCVwzsXF2S_Mv_h7wPM1h1w4_1whS4MDKO-_tMNah4fmvop2b9ud3W1T0DmPOElUZISXSrTMSM70hWZkkL1HZSiFxmXBc-lXK6QGWEFMxpE3hMDguWkk2yDbn-xa_H2Y4kK4bs9D9CuA7Af1fRX2Q</recordid><startdate>20211129</startdate><enddate>20211129</enddate><creator>Ada, Suzan Ece</creator><creator>Seker, M. Yunus</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20211129</creationdate><title>Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool</title><author>Ada, Suzan Ece ; Seker, M. Yunus</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a674-490d610e9dfb3f6b0b82d65dcba95c5246847666845620383fea57c0fa5370b63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Computer Vision and Pattern Recognition</topic><topic>Computer Science - Graphics</topic><topic>Computer Science - Learning</topic><topic>Computer Science - Neural and Evolutionary Computing</topic><toplevel>online_resources</toplevel><creatorcontrib>Ada, Suzan Ece</creatorcontrib><creatorcontrib>Seker, M. Yunus</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Ada, Suzan Ece</au><au>Seker, M. Yunus</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool</atitle><date>2021-11-29</date><risdate>2021</risdate><abstract>Sketches are abstract representations of visual perception and visuospatial
construction. In this work, we proposed a new framework, Generative Adversarial
Networks with Conditional Neural Movement Primitives (GAN-CNMP), that
incorporates a novel adversarial loss on CNMP to increase sketch smoothness and
consistency. Through the experiments, we show that our model can be trained
with few unlabeled samples, can construct distributions automatically in the
latent space, and produces better results than the base model in terms of shape
consistency and smoothness.</abstract><doi>10.48550/arxiv.2111.14934</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition Computer Science - Graphics Computer Science - Learning Computer Science - Neural and Evolutionary Computing |
| title | Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool |
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