Abstract
Vector graphics are widely used in digital art and highly favored by designers due to their scalability and layer-wise properties. However, the process of creating and editing vector graphics requires creativity and design expertise, making it a time-consuming task. Recent advancements in text-to-vector (T2V) generation have aimed to make this process more accessible. However, existing T2V methods directly optimize control points of vector graphics paths, often resulting in intersecting or jagged paths due to the lack of geometry constraints. To overcome these limitations, we propose a novel neural path representation by designing a dual-branch Variational Autoencoder (VAE) that learns the path latent space from both sequence and image modalities. By optimizing the combination of neural paths, we can incorporate geometric constraints while preserving expressivity in generated SVGs. Furthermore, we introduce a two-stage path optimization method to improve the visual and topological quality of generated SVGs. In the first stage, a pre-trained text-to-image diffusion model guides the initial generation of complex vector graphics through the Variational Score Distillation (VSD) process. In the second stage, we refine the graphics using a layer-wise image vectorization strategy to achieve clearer elements and structure. We demonstrate the effectiveness of our method through extensive experiments and showcase various applications. The project page is this https URL.
Abstract (translated)
向量图形广泛应用于数字艺术领域，并因其可扩展性和分层特性而受到设计师的青睐。然而，创建和编辑向量图形需要创造力和设计专业知识，使得这是一个耗时任务。近年来，在文本到向量（T2V）生成方面的先进技术旨在使这个过程更加容易。然而，现有的T2V方法直接优化向量图形的控制点，往往导致路径交叉或交错，由于缺乏几何约束。为了克服这些限制，我们提出了一种新颖的神经路径表示方法，通过设计一种双分支变分自编码器（VAE），从序列和图像模态中学习路径潜在空间。通过优化神经路径的组合，我们可以同时包含几何约束，同时保留生成的SVG的表现力。此外，我们还引入了双阶段路径优化方法，以改善生成的SVG的视觉和拓扑质量。在第一阶段，预训练的文本到图像扩散模型指导复杂向量图形通过变分分数蒸馏（VSD）过程进行初始生成。在第二阶段，我们通过分层图像向量化策略来优化图形，以实现更清晰的元素和结构。我们通过广泛的实验来证明我们方法的的有效性，并展示各种应用。这个项目页面是 https://url.com/。
URL
https://arxiv.org/abs/2405.10317
