ニュース

Title：Continual Few-shot Patch-based Learning for Anime-style Colorization

Presenter(s)：Akinobu Maejima (OLM Digital, IMAGICA GROUP), Seitaro Shinagawa(NAIST), Hiroyuki Kubo (Chiba University), Takuya Funatomi (NAIST),Tatsuo Yotsukura (OLM Digital, IMAGICA GROUP), Satoshi Nakamura,Yasuhiro Mukaigawa (NAIST)

Description：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, collecting many 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 continual 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 only using a few pre- and post-colorized line drawings that are created by artists in their usual colorization work. Therefore, our method can be easily implemented into existing production pipelines. We demonstrated that our colorization method outperformed state-of-the-art methods using a quantitative evaluation.