基于少镜头补丁的连续学习,实现动画风格着色

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Akinobu Maejima, Seitaro Shinagawa, Hiroyuki Kubo, Takuya Funatomi, Tatsuo Yotsukura, Satoshi Nakamura, Yasuhiro Mukaigawa
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引用次数: 0

摘要

动漫线稿的自动着色是生产流水线中的一个难题。深度神经网络的最新进展已经解决了这一问题;然而,在着色过程开始前收集大量新颖动漫作品中着色目标的图像会导致鸡生蛋、蛋生鸡的问题,成为在生产流水线中使用它们的障碍。为了克服这一障碍,我们提出了一种新的基于补丁的动漫风格着色学习方法。该学习方法采用了高效的补丁采样技术,并根据动漫线图的特点进行了位置嵌入。我们还提出了一种持续学习策略,利用人类艺术家着色的新样本不断更新我们的着色模型。我们的方法的优势在于,它可以从头开始学习我们的着色模型,或者只使用一些着色前和着色后的线条图来预先训练权重,这些线条图是由艺术家们在日常着色工作中创建的。因此,我们的方法可以很容易地集成到现有的生产流水线中。我们通过定量分析证明,我们的着色方法优于最先进的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Continual few-shot patch-based learning for anime-style colorization

Continual few-shot patch-based learning for anime-style colorization

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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来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
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