Dongchi Li

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In European Conference on Computer Vision, pp. 55–70. Cited by: Figure 12, Figure 12, Figure 13, Figure 13, Appendix B. [41] L. Zhang, A. Rao, and M. Agrawala (2023) Adding conditional control to text-to-image diffusion models. Cited by: §2.1. Supplementary Material Appendix A Anime Illustration Layers In professional anime production workflows, the decomposition of illustrations into functional layers is essential for a non-destructive and collaborative creative process. The line art layer serves as the geometric foundation by defining the structural topology and semantic boundaries that guide all subsequent rendering stages. Building upon this base, the flat color layer encapsulates the local albedo of each region to act as a pixel-level mask, which allows for precise color adjustments without affecting neighboring areas. To simulate volume and environmental lighting, the shadow layer is isolated to capture ambient occlusion and directional shading for providing depth to the flat regions. Finally, the highlight layer is used to define the properties of the material and specular reflections that include the metallic luster of the accessories or the expressive glints in the eyes of a character. Specifically, as shown in Fig. 10, shadow layers exhibit different visual expressions depending on the composition method. In anime illustration, Normal and Multiply are the two most common modes. Normal blending is the most universal and intuitive approach, as it performs direct mixing based on Alpha values. However, if a texture is inserted under a Normal shadow layer, the texture will be obscured by the opaque regions of the shadow. By switching to Multiply mode, the underlying texture can be clearly revealed. Therefore, adopting Multiply blending significantly enhances the versatility of the shadow layer. While these different expressions can be converted into one another, our current method primarily focuses on shadows under Normal composition. We plan to expand our dataset in the future to discuss a wider variety of expression modes. Appendix B More Visual Results In this section, we present extended dataset samples, comparative studies against state-of-the-art baselines, and more ablation experiments. These results further demonstrate our model’s robustness in extracting functional layers. Dataset. As shown in Fig. 11, we present additional data samples from our dataset. All assets have been meticulously hand-drawn by professional artists and properly organized into four functional layers. The source files are provided in PSD format, which technically allows for high-resolution training. However, to maintain an optimal balance between computational efficiency and output quality, we opted for a training resolution of