为动漫插画设计分层分解框架，提升生成可控性。
Workflow-Aware Structured Layer Decomposition for Illustration Production
- 按动漫制作流程拆解图像为线稿、平涂、阴影、高光等语义层
- 引入轻量级语义嵌入与逐层损失函数，实现精准解耦
- 构建模拟动漫生产流程的数据集，支持后续着色与纹理嵌入
近期的生成式图像编辑方法采用分层表示以缓解位图图像的纠缠问题并提升可控性，通常依赖于基于物体的分割。然而，此类策略可能无法捕捉人类创作图像（如动漫插画）的结构化与风格化特征。为此，我们提出一种面向动漫插画制作流程的结构化分层分解框架。受动漫制作流程启发，该方法将插画分解为线稿、平涂、阴影和高光等语义明确的生产层。为解耦各层，引入轻量级层语义嵌入，为每层提供特定任务引导，并设计一组逐层损失函数监督训练过程。针对缺乏真实分层数据的问题，构建了一个高质量的动漫插画数据集，模拟标准动画制作流程。实验表明，本方法实现了准确且视觉一致的分层分解。我们认为所得分层表示可进一步支持下游任务，如重着色与纹理嵌入，助力内容创作与插画编辑。代码已开源：https://github.com/zty0304/Anime-layer-decomposition
原文摘要 · Abstract (English)
Recent generative image editing methods adopt layered representations to mitigate the entangled nature of raster images and improve controllability, typically relying on object-based segmentation. However, such strategies may fail to capture the structural and stylized properties of human-created images, such as anime illustrations. To solve this issue, we propose a workflow-aware structured layer decomposition framework tailored to the illustration production of anime artwork. Inspired by the creation pipeline of anime production, our method decomposes the illustration into semantically meaningful production layers, including line art, flat color, shadow, and highlight. To decouple all these layers, we introduce lightweight layer semantic embeddings to provide specific task guidance for each layer. Furthermore, a set of layer-wise losses is incorporated to supervise the training process of individual layers. To overcome the lack of ground-truth layered data, we construct a high-quality illustration dataset that simulated the standard anime production workflow. Experiments demonstrate that the accurate and visually coherent layer decompositions were achieved by using our method. We believe that the resulting layered representation further enables downstream tasks such as recoloring and embedding texture, supporting content creation, and illustration editing. Code is available at: https://github.com/zty0304/Anime-layer-decomposition
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