发表机构
The University of Tokyo; OLM Digital, Inc.; IMAGICA GROUP Inc.（东京大学; OLM数字公司; IMAGICA集团公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对动漫上色中微小间隙的手动处理难题，研究人员推出基于专业实践的GapFill工具，采用深度学习参考周围区域推荐填充色，经13名专业上色师验证，其性能与可用性优于传统方法，可作为现有工具的补充。
AI 中文摘要
动画制作流程常涉及线稿的数字上色，其中小的未上色区域（“间隙”）是尚未得到充分探索的挑战。我们在日本动画（anime）制作流程中开展了一项形成性研究，发现尽管油漆桶工具被广泛用于基础上色，但微小的封闭区域常被忽略，导致耗时的手动检测与填充。我们推出GapFill，一款基于专业实践的工具，可减少间隙检测、缩放及颜色选择的工作量。我们的深度学习方法利用动漫风格图像的平色特性，通过参考周围区域推荐合适的填充颜色。在有13名专业上色师参与的用户研究中，我们的系统在间隙填充任务上的性能和可用性优于传统方法。该研究还表明，仅预测精度并非可用性的主要因素，合适的颜色在语境中可能存在歧义，且GapFill可根据用户对新型AI辅助工具的信任度，作为现有工具的补充。
英文摘要
Animation production workflows often involve digital colorization of line art, where small unpainted regions ("gaps") frequently occur and remain an underexplored challenge. We conducted a formative study in Japanese animation (anime) pipelines and found that while the paint bucket tool is widely used for base coloring, tiny enclosed areas are frequently overlooked, resulting in time-consuming manual detection and filling. We introduce GapFill, a tool grounded in professional practices that reduces the effort of gap detection, zooming, and color selection. Our deep-learning method suggests appropriate fill colors by referencing surrounding regions, leveraging the flat-color nature of anime-style images. In a user study with 13 professional colorists, our system improved performance and usability in gap-filling tasks over conventional methods. The study also suggested that prediction accuracy alone is not the primary factor for usability, that appropriate colors can be contextually ambiguous, and that GapFill can complement existing tools depending on users' trust in new AI-powered assistance.
Comments19 pages, 20 figures. Published in the Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26)
Journal refProceedings of the 2026 CHI Conference on Human Factors in Computing Systems (CHI '26), ACM, New York, NY, USA, 2026, 19 pages
