VectorPainter: Advanced Stylized Vector Graphics Synthesis Using Stroke-Style Priors

TL;DR

VectorPainter is a new framework that synthesizes stylized vector graphics from text and reference images by rearranging vectorized strokes, employing style-preserving techniques for high-quality, artist-specific results.

Contribution

It introduces a novel stroke-based vectorization and synthesis method that captures and preserves artistic styles through imitation learning and specialized loss functions.

Findings

Outperforms existing stylized vector graphics synthesis methods

Effectively captures and reproduces artistic stroke styles

Demonstrates high-quality, style-preserving vector graphic generation

Abstract

We introduce VectorPainter, a novel framework designed for reference-guided text-to-vector-graphics synthesis. Based on our observation that the style of strokes can be an important aspect to distinguish different artists, our method reforms the task into synthesize a desired vector graphics by rearranging stylized strokes, which are vectorized from the reference images. Specifically, our method first converts the pixels of the reference image into a series of vector strokes, and then generates a vector graphic based on the input text description by optimizing the positions and colors of these vector strokes. To precisely capture the style of the reference image in the vectorized strokes, we propose an innovative vectorization method that employs an imitation learning strategy. To preserve the style of the strokes throughout the generation process, we introduce a style-preserving loss function. Extensive experiments have been conducted to demonstrate the superiority of our approach over existing works in stylized vector graphics synthesis, as well as the effectiveness of the various components of our method.