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  Manipulating Attributes of Natural Scenes via Hallucination

Karacan, L., Akata, Z., Erdem, A., & Erdem, E. (2018). Manipulating Attributes of Natural Scenes via Hallucination. Retrieved from http://arxiv.org/abs/1808.07413.

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arXiv:1808.07413.pdf (Preprint), 9MB
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 Creators:
Karacan, Levent1, Author
Akata, Zeynep2, Author              
Erdem, Aykut1, Author
Erdem, Erkut1, Author
Affiliations:
1External Organizations, ou_persistent22              
2Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society, ou_1116547              

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Free keywords: Computer Science, Computer Vision and Pattern Recognition, cs.CV
 Abstract: In this study, we explore building a two-stage framework for enabling users to directly manipulate high-level attributes of a natural scene. The key to our approach is a deep generative network which can hallucinate images of a scene as if they were taken at a different season (e.g. during winter), weather condition (e.g. in a cloudy day) or time of the day (e.g. at sunset). Once the scene is hallucinated with the given attributes, the corresponding look is then transferred to the input image while preserving the semantic details intact, giving a photo-realistic manipulation result. As the proposed framework hallucinates what the scene will look like, it does not require any reference style image as commonly utilized in most of the appearance or style transfer approaches. Moreover, it allows to simultaneously manipulate a given scene according to a diverse set of transient attributes within a single model, eliminating the need of training multiple networks per each translation task. Our comprehensive set of qualitative and quantitative results demonstrate the effectiveness of our approach against the competing methods.

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Language(s): eng - English
 Dates: 2018-08-222018
 Publication Status: Published online
 Pages: 15 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 1808.07413
URI: http://arxiv.org/abs/1808.07413
BibTex Citekey: Karacan_2018
 Degree: -

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