TexSketch: Bringing Texture-Aware Colorization to Sketches
2026-07-27 • Graphics
Graphics
AI summaryⓘ
The authors created TexSketch, a system that automatically makes colored sketches without needing big hand-labeled datasets. Instead of copying specific art styles from real examples, their method uses rules and computer graphics to program different styles. This helps create lots of varied, believable colored sketches for training colorization models, without the heavy work of manual drawing or labeling. People found these generated sketches look real and artistically diverse.
sketch colorizationprocedural generationshader-based renderingsemantic color predictiondataset annotationgeometric analysisartistic stylesynthetic datacomputer graphics
Authors
Taraash Mittal, Gaurav Rai, Ojaswa Sharma
Abstract
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.