VecFusion：使用扩散生成矢量字体

VecFusion: Vector Font Generation with Diffusion

December 16, 2023

作者: Vikas Thamizharasan, Difan Liu, Shantanu Agarwal, Matthew Fisher, Michael Gharbi, Oliver Wang, Alec Jacobson, Evangelos Kalogerakis

cs.AI

摘要

我们提出了VecFusion，这是一种新的神经架构，可以生成具有不同拓扑结构和精确控制点位置的矢量字体。我们的方法是一个级联扩散模型，包括一个光栅扩散模型和一个矢量扩散模型。光栅模型生成低分辨率的光栅化字体，并带有辅助控制点信息，捕捉字体的全局风格和形状，而矢量模型则根据第一阶段的低分辨率光栅字体合成矢量字体。为了合成长且复杂的曲线，我们的矢量扩散模型采用了变压器架构和一种新颖的矢量表示，使得能够对多样的矢量几何进行建模，并精确预测控制点。我们的实验表明，与先前用于矢量图形的生成模型相比，我们的新级联矢量扩散模型生成了质量更高、具有复杂结构和多样风格的矢量字体。

English

We present VecFusion, a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions. Our approach is a cascaded diffusion model which consists of a raster diffusion model followed by a vector diffusion model. The raster model generates low-resolution, rasterized fonts with auxiliary control point information, capturing the global style and shape of the font, while the vector model synthesizes vector fonts conditioned on the low-resolution raster fonts from the first stage. To synthesize long and complex curves, our vector diffusion model uses a transformer architecture and a novel vector representation that enables the modeling of diverse vector geometry and the precise prediction of control points. Our experiments show that, in contrast to previous generative models for vector graphics, our new cascaded vector diffusion model generates higher quality vector fonts, with complex structures and diverse styles.