ReGO: Reference-Guided Outpainting for Scenery Image.

Wang, Y; Wei, Y; Qian, X; Zhu, L; Yang, Y

ReGO: Reference-Guided Outpainting for Scenery Image.

Wang, Y Wei, Y Qian, X Zhu, L Yang, Y

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Trans Image Process, 2024, 33, pp. 1375-1388
Issue Date:: 2024

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Field	Value	Language
dc.contributor.author	Wang, Y
dc.contributor.author	Wei, Y
dc.contributor.author	Qian, X
dc.contributor.author	Zhu, L
dc.contributor.author	Yang, Y https://orcid.org/0000-0002-0512-880X
dc.date.accessioned	2024-08-07T04:59:35Z
dc.date.available	2024-08-07T04:59:35Z
dc.date.issued	2024
dc.identifier.citation	IEEE Trans Image Process, 2024, 33, pp. 1375-1388
dc.identifier.issn	1057-7149
dc.identifier.issn	1941-0042
dc.identifier.uri	http://hdl.handle.net/10453/180257
dc.description.abstract	We present ReGO (Reference-Guided Outpainting), a new method for the task of sketch-guided image outpainting. Despite the significant progress made in producing semantically coherent content, existing outpainting methods often fail to deliver visually appealing results due to blurry textures and generative artifacts. To address these issues, ReGO leverages neighboring reference images to synthesize texture-rich results by transferring pixels from them. Specifically, an Adaptive Content Selection (ACS) module is incorporated into ReGO to facilitate pixel transfer for texture compensating of the target image. Additionally, a style ranking loss is introduced to maintain consistency in terms of style while preventing the generated part from being influenced by the reference images. ReGO is a model-agnostic learning paradigm for outpainting tasks. In our experiments, we integrate ReGO with three state-of-the-art outpainting models to evaluate its effectiveness. The results obtained on three scenery benchmarks, i.e. NS6K, NS8K and SUN Attribute, demonstrate the superior performance of ReGO compared to prior art in terms of texture richness and authenticity. Our code is available at https://github.com/wangyxxjtu/ReGO-Pytorch.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Trans Image Process
dc.relation.isbasedon	10.1109/TIP.2024.3357290
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0906 Electrical and Electronic Engineering, 1702 Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	4603 Computer vision and multimedia computation
dc.subject.classification	4607 Graphics, augmented reality and games
dc.title	ReGO: Reference-Guided Outpainting for Scenery Image.
dc.type	Journal Article
utslib.citation.volume	33
utslib.location.activity	United States
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1702 Cognitive Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-07T04:59:30Z
pubs.publication-status	Published
pubs.volume	33

Abstract:

We present ReGO (Reference-Guided Outpainting), a new method for the task of sketch-guided image outpainting. Despite the significant progress made in producing semantically coherent content, existing outpainting methods often fail to deliver visually appealing results due to blurry textures and generative artifacts. To address these issues, ReGO leverages neighboring reference images to synthesize texture-rich results by transferring pixels from them. Specifically, an Adaptive Content Selection (ACS) module is incorporated into ReGO to facilitate pixel transfer for texture compensating of the target image. Additionally, a style ranking loss is introduced to maintain consistency in terms of style while preventing the generated part from being influenced by the reference images. ReGO is a model-agnostic learning paradigm for outpainting tasks. In our experiments, we integrate ReGO with three state-of-the-art outpainting models to evaluate its effectiveness. The results obtained on three scenery benchmarks, i.e. NS6K, NS8K and SUN Attribute, demonstrate the superior performance of ReGO compared to prior art in terms of texture richness and authenticity. Our code is available at https://github.com/wangyxxjtu/ReGO-Pytorch.

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http://hdl.handle.net/10453/180257