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With the rapid development of digital media technologies, the methods of recording and expressing human experiences have undergone revolutionary transformation—from the basic linear depiction in the original cave paintings to realistic renderings in the Renaissance, and then to today's high-definition color images and short-format video content. Visual performance continues to develop toward higher resolution, richer color palettes, and enhanced expressive force. Against this context, it has become an important research direction in the field of computer vision and digital media to meet the growing aesthetic needs of the audience through technological innovation and creation of visually layered and artistically striking effects (Endo et al., 2020; Muratbekova & Shamoi, 2024). Excellent visual creativity can not only enhance users' participation and satisfaction but also endow products with unique value, thus significantly improving their market competitiveness (Radford & Bloch, 2011). As a medium combining artistic expression and narrative function, animation advocates play an irreplaceable role in fields of film entertainment, digital education, game development, and so on (Wang & Zhong, 2024; Xu et al. 2022). The explosive growth of short video platforms and streaming media services further promotes the unprecedented demand for high-quality animation content (Lu, 2024). In particular, fueled by the rapid expansion of these platforms in recent years, the global animation market continues to grow, and the demand for high-quality, low-cost, fast-delivery animation works is growing exponentially. However, within the industrial animation production pipeline, the coloring stage remains a persistent bottleneck that constrains both production efficiency and artistic quality. Traditional manual coloring methods suffer from two inherent limitations. First, professional animators must painstakingly apply color to each frame, with standard projects often requiring the processing of tens or even hundreds of thousands of original drawings. This labor-intensive approach leads to prohibitively high production costs. Secondly, due to subjective differences of different artists in color interpretation, there are often inconsistencies between different batches of pictures in the same work, which greatly damages the visual coherence (Chen et al. 2022; Junjun & Pillai, 2024).
While existing automated coloring tools based on conventional image-processing algorithms can improve baseline efficiency to some extent, these methods rely solely on low-level image features (e.g., luminance, texture) for mechanical color filling (Žeger et al., 2021). They lack the ability to understand the semantic relationships between characters and scenes, not to mention achieving the precise artistic application required by directors. Consequently, outputs often exhibit professional shortcomings, such as color bleeding, detail loss, and stylistic mismatch, falling markedly short of commercial animation standards. This prevailing situation underscores the urgent need for next-generation intelligent coloring technologies. By deeply integrating deep learning with computer graphics methodologies, the development of coloring systems capable of semantic understanding, artistic style adaptation, and color consistency maintenance promises to revolutionize animation production workflows, while unlocking new possibilities for digital content creation.
The rapid progress of deep learning technology provides new opportunities for solving the pain points in these industries. In particular, the combined application of convolutional neural networks (CNN) (Jing et al. 2019) and a generation countermeasure network, i.e., a generative adversarial network (GAN) (Nazeree & Ibrahimi, 2018), has made breakthroughs in the semantic understanding and generation quality of images. However, it is still very limited to directly apply the existing image coloring technology to animation scenes. Animation sequences demand exceptionally high color coherence between frames, character designs require precise detail preservation, and commercial production emphasizes flexible adaptation to diverse artistic styles. These professional requirements impose far more stringent standards on automatic coloring technology than those for static image processing.