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2016 | OriginalPaper | Buchkapitel

Pixel-Level Domain Transfer

verfasst von : Donggeun Yoo, Namil Kim, Sunggyun Park, Anthony S. Paek, In So Kweon

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

We present an image-conditional image generation model. The model transfers an input domain to a target domain in semantic level, and generates the target image in pixel level. To generate realistic target images, we employ the real/fake-discriminator as in Generative Adversarial Nets [6], but also introduce a novel domain-discriminator to make the generated image relevant to the input image. We verify our model through a challenging task of generating a piece of clothing from an input image of a dressed person. We present a high quality clothing dataset containing the two domains, and succeed in demonstrating decent results.

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Vorheriges Kapitel Real-Time Large-Scale Dense 3D Reconstruction with Loop Closure

Nächstes Kapitel Accelerating Convolutional Neural Networks with Dominant Convolutional Kernel and Knowledge Pre-regression

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Titel: Pixel-Level Domain Transfer
verfasst von: Donggeun Yoo
Namil Kim
Sunggyun Park
Anthony S. Paek
In So Kweon
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2016
Print ISBN: 978-3-319-46483-1

Electronic ISBN: 978-3-319-46484-8

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-46484-8_31

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