TexSketch：为草图带来纹理感知的色彩化
TexSketch: Bringing Texture-Aware Colorization to Sketches
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中文总结 AI 辅助
针对现有草图色彩化数据集的问题，提出TexSketch框架，通过几何分析和着色器驱动风格化生成彩色草图数据集，全自动管道集成多种功能，无需人工标注等，生成的草图风格多样，为草图色彩化提供合成监督来源。
中文摘要 AI 辅助
基于参考的草图色彩化方法依赖大型配对数据集，现有的数据集存在规模有限、标注昂贵且存在风格偏差等问题。本文提出TexSketch，一个通过几何分析和着色器驱动的风格化来生成具有可编程艺术风格的彩色草图数据集的可控程序框架。其全自动管道集成了区域提取、语义颜色预测和基于着色器的渲染。通过程序定义艺术外观，无需人工标注或艺术家监督。人类研究表明，TexSketch生成的彩色草图具有高度风格多样性且视觉上合理，为草图色彩化提供了可控、可扩展的合成监督来源。
英文摘要
Reference-based sketch colorization methods rely on large paired datasets that preserve both the structural and stylistic characteristics of hand-drawn artwork. However, existing datasets are limited in scale, expensive to annotate, and bound to fixed, often inconsistent artistic style biases that propagate to downstream models and limit cross-domain generalization. We present TexSketch, a controllable procedural framework for generating colored-sketch datasets with programmable artistic styles via geometric analysis and shader-driven stylization. Our fully automatic pipeline integrates region extraction, semantic color prediction, and shader-based rendering. By defining artistic appearance procedurally rather than inheriting it from a static corpus, TexSketch enables scalable dataset generation without manual annotation or artist supervision. Human studies demonstrate that TexSketch generates perceptually plausible colored sketches with high stylistic diversity, providing a controllable, scalable source of synthetic supervision for sketch colorization.
