Vectorized Region Based Brush Strokes for Artistic Rendering

TL;DR

This paper introduces a novel image-to-painting method that uses vectorized, region-based brush strokes to produce artistic renderings with high fidelity, guided by semantic regions and stroke sequencing.

Contribution

It presents a new approach that combines semantic guidance, stroke parameter computation, and stroke sequencing to improve artistic rendering quality.

Findings

High fidelity and stroke quality in generated paintings

Effective semantic guidance for targeted regions

Versatile application across different image types

Abstract

Creating a stroke-by-stroke evolution process of a visual artwork tries to bridge the emotional and educational gap between the finished static artwork and its creation process. Recent stroke-based painting systems focus on capturing stroke details by predicting and iteratively refining stroke parameters to maximize the similarity between the input image and the rendered output. However, these methods often struggle to produce stroke compositions that align with artistic principles and intent. To address this, we explore an image-to-painting method that (i) facilitates semantic guidance for brush strokes in targeted regions, (ii) computes the brush stroke parameters, and (iii) establishes a sequence among segments and strokes to sequentially render the final painting. Experimental results on various input image types, such as face images, paintings, and photographic images, show that our method aligns with a region-based painting strategy while rendering a painting with high fidelity and superior stroke quality.