Very long natural scenery image prediction by outpainting

Yang, Z; Dong, J; Liu, P; Yang, Y; Yan, S

Very long natural scenery image prediction by outpainting

Yang, Z

Dong, J Liu, P

Yang, Y

Yan, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of the IEEE International Conference on Computer Vision, 2020, 2019-October, pp. 10560-10569
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Yang, Z https://orcid.org/0000-0001-8783-8313
dc.contributor.author	Dong, J
dc.contributor.author	Liu, P https://orcid.org/0000-0002-3170-3783
dc.contributor.author	Yang, Y https://orcid.org/0000-0002-0512-880X
dc.contributor.author	Yan, S
dc.date	2019-10-27
dc.date.accessioned	2021-04-12T10:55:20Z
dc.date.available	2021-04-12T10:55:20Z
dc.date.issued	2020
dc.identifier.citation	Proceedings of the IEEE International Conference on Computer Vision, 2020, 2019-October, pp. 10560-10569
dc.identifier.isbn	9781728148038
dc.identifier.issn	1550-5499
dc.identifier.issn	2380-7504
dc.identifier.uri	http://hdl.handle.net/10453/148040
dc.description.abstract	© 2019 IEEE. Comparing to image inpainting, image outpainting receives less attention due to two challenges in it. The first challenge is how to keep the spatial and content consistency between generated images and original input. The second challenge is how to maintain high quality in generated results, especially for multi-step generations in which generated regions are spatially far away from the initial input. To solve the two problems, we devise some innovative modules, named Skip Horizontal Connection and Recurrent Content Transfer, and integrate them into our designed encoder-decoder structure. By this design, our network can generate highly realistic outpainting prediction effectively and efficiently. Other than that, our method can generate new images with very long sizes while keeping the same style and semantic content as the given input. To test the effectiveness of the proposed architecture, we collect a new scenery dataset with diverse, complicated natural scenes. The experimental results on this dataset have demonstrated the efficacy of our proposed network.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	Proceedings of the IEEE International Conference on Computer Vision
dc.relation.ispartof	IEEE/CVF International Conference on Computer Vision
dc.relation.isbasedon	10.1109/ICCV.2019.01066
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Very long natural scenery image prediction by outpainting
dc.type	Conference Proceeding
utslib.citation.volume	2019-October
utslib.location.activity	Seoul, Korea (South)
utslib.for	0801 Artificial Intelligence and Image Processing
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-04-12T10:55:18Z
pubs.finish-date	2019-11-02
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2019-10-27
pubs.volume	2019-October
dc.location	Piscataway, USA

Abstract:

© 2019 IEEE. Comparing to image inpainting, image outpainting receives less attention due to two challenges in it. The first challenge is how to keep the spatial and content consistency between generated images and original input. The second challenge is how to maintain high quality in generated results, especially for multi-step generations in which generated regions are spatially far away from the initial input. To solve the two problems, we devise some innovative modules, named Skip Horizontal Connection and Recurrent Content Transfer, and integrate them into our designed encoder-decoder structure. By this design, our network can generate highly realistic outpainting prediction effectively and efficiently. Other than that, our method can generate new images with very long sizes while keeping the same style and semantic content as the given input. To test the effectiveness of the proposed architecture, we collect a new scenery dataset with diverse, complicated natural scenes. The experimental results on this dataset have demonstrated the efficacy of our proposed network.

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http://hdl.handle.net/10453/148040