Continual few-shot patch-based learning for anime-style colorization Article Swipe
Akinobu Maejima
,
Seitaro Shinagawa
,
Hiroyuki Kubo
,
Takuya Funatomi
,
Tatsuo Yotsukura
,
Satoshi Nakamura
,
Yasuhiro Mukaigawa
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s41095-024-0414-4
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s41095-024-0414-4
The automatic colorization of anime line drawings is a challenging problem in production pipelines. Recent advances in deep neural networks have addressed this problem; however, collectingmany images of colorization targets in novel anime work before the colorization process starts leads to chicken-and-egg problems and has become an obstacle to using them in production pipelines. To overcome this obstacle, we propose a new patch-based learning method for few-shot anime-style colorization. The learning method adopts an efficient patch sampling technique with position embedding according to the characteristics of anime line drawings. We also present a continuous learning strategy that continuously updates our colorization model using new samples colorized by human artists. The advantage of our method is that it can learn our colorization model from scratch or pre-trained weights using only a few pre- and post-colorized line drawings that are created by artists in their usual colorization work. Therefore, our method can be easily incorporated within existing production pipelines. We quantitatively demonstrate that our colorizationmethod outperforms state-of-the-art methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s41095-024-0414-4
- https://link.springer.com/content/pdf/10.1007/s41095-024-0414-4.pdf
- OA Status
- diamond
- Cited By
- 3
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400465956
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4400465956Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s41095-024-0414-4Digital Object Identifier
- Title
-
Continual few-shot patch-based learning for anime-style colorizationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-09Full publication date if available
- Authors
-
Akinobu Maejima, Seitaro Shinagawa, Hiroyuki Kubo, Takuya Funatomi, Tatsuo Yotsukura, Satoshi Nakamura, Yasuhiro MukaigawaList of authors in order
- Landing page
-
https://doi.org/10.1007/s41095-024-0414-4Publisher landing page
- PDF URL
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https://link.springer.com/content/pdf/10.1007/s41095-024-0414-4.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s41095-024-0414-4.pdfDirect OA link when available
- Concepts
-
Anime, Computer science, Obstacle, Artificial intelligence, Embedding, Scratch, Line (geometry), Deep learning, Process (computing), Style (visual arts), Graphics, Computer vision, Special effects, Computer graphics (images), Visual arts, Art, Mathematics, Geometry, Operating system, Law, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
- References (count)
-
19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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