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Title
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Multiple exemplars-based hallucination for face super-resolution and editing
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Author
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Abstract
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| Given a really low resolution input image of a face (say 16×16 or 8×8 pixels), the goal of this paper is to reconstruct a high-resolution version thereof. This, by itself, is an ill-posed problem, as the high-frequency information is missing in the low-resolution input and needs to be hallucinated, based on prior knowledge about the image content. Rather than relying on a generic face prior, in this paper we explore the use of a set of exemplars, i.e. other high-resolution images of the same person. These guide the neural network as we condition the output on them. Multiple exemplars work better than a single one. To combine the information from multiple exemplars effectively, we introduce a pixel-wise weight generation module. Besides standard face super-resolution, our method allows to perform subtle face editing simply by replacing the exemplars with another set with different facial features. A user study is conducted and shows the super-resolved images can hardly be distinguished from real images on the CelebA dataset. A qualitative comparison indicates our model outperforms methods proposed in the literature on the CelebA and WebFace datasets. |
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Language
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English
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Source (book)
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Computer Vision : ACCV 2020 15th Asian Conference on Computer Vision, Kyoto, Japan, November 30 – December 4, 2020, Revised Selected Papers, Part V
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Publication
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Cham
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Springer
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2021
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ISBN
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978-3-030-69540-8
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DOI
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10.1007/978-3-030-69541-5_16
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Volume/pages
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p. 258-273
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Full text (Publisher's DOI)
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