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- AOSHIMA Takehiro
- Graduate School of Engineering Science, Osaka University
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- MATSUBARA Takashi
- Graduate School of Engineering Science, Osaka University
この論文をさがす
抄録
<p>The creation of images and other data is one of the ultimate goals of computer vision research. For this purpose, various deep learning methods have been proposed, such as variational autoencoders, adversarial networks, and diffusion models. These methods learn the distributions of photographs and illustrations and reproduce them. The generated image is determined using the coordinates provided in the latent space. Therefore, several studies have been conducted to manipulate these coordinates to edit the generated images. However, existing methods frequently provide unintended or low-quality editing results because the coordinate system in the latent space is not properly learned, among other reasons. In this study, we focus on the coordinate system in the representation space and introduce deep curvilinear editing. In particular, we propose a method for the representation vectors using representation space with a curvilinear coordinate system. The method was also combined with generative adversarial networks, whose results demonstrated that the proposed method enables the high-quality editing of generated images.</p>
収録刊行物
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- 日本画像学会誌
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日本画像学会誌 62 (6), 579-587, 2023-12-10
一般社団法人 日本画像学会
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詳細情報 詳細情報について
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- CRID
- 1390298433281727616
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- NII書誌ID
- AA1137305X
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- ISSN
- 18804675
- 13444425
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- NDL書誌ID
- 033225815
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
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- 抄録ライセンスフラグ
- 使用不可