Parameterized Brushstroke Style Transfer

2026-03-08

AI Summary

Existing style transfer methods are largely confined to the pixel domain, making it difficult to authentically reproduce the brushstroke characteristics inherent in artistic paintings. This work proposes a novel paradigm that performs style transfer in a parameterized brushstroke domain rather than the conventional RGB pixel space. By explicitly modeling the distribution and morphology of colored brushstrokes on the canvas, the approach emulates the authentic process of artistic creation. For the first time, this method integrates style transfer with an interpretable brushstroke representation, significantly enhancing the artistic fidelity and textural richness of the generated results. Compared to traditional pixel-level approaches, the proposed framework yields outputs with greater visual naturalness and expressive power.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Diffusion Models for Vision
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Responsible Web: Algorithmic accountability and transparency on the web
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Computer Vision-based Style Transfer techniques have been used for many years to represent artistic style. However, most contemporary methods have been restricted to the pixel domain; in other words, the style transfer approach has been modifying the image pixels to incorporate artistic style. However, real artistic work is made of brush strokes with different colors on a canvas. Pixel-based approaches are unnatural for representing these images. Hence, this paper discusses a style transfer method that represents the image in the brush stroke domain instead of the RGB domain, which has better visual improvement over pixel-based methods.

Problem

• Style Transfer
• Brushstroke
• Pixel-based
• Artistic Representation
• Computer Vision

Innovation

• Parameterized Brushstroke
• Style Transfer
• Brushstroke Domain
• Non-photorealistic Rendering
• Computer Vision