A Layer-Based Sequential Framework for Scene Generation with GANs

Mehmet Ozgur Turkoglu, Luuk Spreeuwers, William Thong, Berkay Kicanaoglu

Research output: Contribution to conferencePaperAcademicpeer-review

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Abstract

The visual world we sense, interpret and interact everyday is a complex composition of interleaved physical entities. Therefore, it is a very challenging task to generate vivid scenes of similar complexity using computers. In this work, we present a scene generation framework based on Generative Adversarial Networks (GANs) to sequentially compose a scene, breaking down the underlying problem into smaller ones. Different than the existing approaches, our framework offers an explicit control over the elements of a scene through separate background and foreground generators. Starting with an initially generated background, foreground objects then populate the scene one-by-one in a sequential manner. Via quantitative and qualitative experiments on a subset of the MS-COCO dataset, we show that our proposed framework produces not only more diverse images but also copes better with affine transformations and occlusion artifacts of foreground objects than its counterparts.
Original languageEnglish
Number of pages9
Publication statusPublished - Feb 2019
EventThirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) - Hilton Hawaiian Village, Honolulu, United States
Duration: 27 Jan 20191 Feb 2019
https://aaai.org/Conferences/AAAI-19/

Conference

ConferenceThirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)
Abbreviated titleAAAI-19
CountryUnited States
CityHonolulu
Period27/01/191/02/19
Internet address

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Turkoglu, M. O., Spreeuwers, L., Thong, W., & Kicanaoglu, B. (2019). A Layer-Based Sequential Framework for Scene Generation with GANs. Paper presented at Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, United States.
Turkoglu, Mehmet Ozgur ; Spreeuwers, Luuk ; Thong, William ; Kicanaoglu, Berkay. / A Layer-Based Sequential Framework for Scene Generation with GANs. Paper presented at Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, United States.9 p.
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Turkoglu, MO, Spreeuwers, L, Thong, W & Kicanaoglu, B 2019, 'A Layer-Based Sequential Framework for Scene Generation with GANs' Paper presented at Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, United States, 27/01/19 - 1/02/19, .

A Layer-Based Sequential Framework for Scene Generation with GANs. / Turkoglu, Mehmet Ozgur; Spreeuwers, Luuk; Thong, William; Kicanaoglu, Berkay.

2019. Paper presented at Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, United States.

Research output: Contribution to conferencePaperAcademicpeer-review

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AB - The visual world we sense, interpret and interact everyday is a complex composition of interleaved physical entities. Therefore, it is a very challenging task to generate vivid scenes of similar complexity using computers. In this work, we present a scene generation framework based on Generative Adversarial Networks (GANs) to sequentially compose a scene, breaking down the underlying problem into smaller ones. Different than the existing approaches, our framework offers an explicit control over the elements of a scene through separate background and foreground generators. Starting with an initially generated background, foreground objects then populate the scene one-by-one in a sequential manner. Via quantitative and qualitative experiments on a subset of the MS-COCO dataset, we show that our proposed framework produces not only more diverse images but also copes better with affine transformations and occlusion artifacts of foreground objects than its counterparts.

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Turkoglu MO, Spreeuwers L, Thong W, Kicanaoglu B. A Layer-Based Sequential Framework for Scene Generation with GANs. 2019. Paper presented at Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), Honolulu, United States.