SVG (Scalable Vector Graphics) is a widely used graphics format that
possesses excellent scalability and editability. Image vectorization, which
aims to convert raster images to SVGs, is an important yet challenging problem
in computer vision and graphics. Existing image vectorization methods either
suffer from low reconstruction accuracy for complex images or require long
computation time. To address this issue, we propose SuperSVG, a
superpixel-based vectorization model that achieves fast and high-precision
image vectorization. Specifically, we decompose the input image into
superpixels to help the model focus on areas with similar colors and textures.
Then, we propose a two-stage self-training framework, where a coarse-stage
model is employed to reconstruct the main structure and a refinement-stage
model is used for enriching the details. Moreover, we propose a novel dynamic
path warping loss to help the refinement-stage model to inherit knowledge from
the coarse-stage model. Extensive qualitative and quantitative experiments
demonstrate the superior performance of our method in terms of reconstruction
accuracy and inference time compared to state-of-the-art approaches. The code
is available in https://github.com/sjtuplayer/SuperSVG.

提出了一种基于超像素的矢量化模型 SuperSVG，它通过将输入图像分解成超像素，采用两阶段自训练框架和动态路径扭曲损失来实现图像的快速高精度矢量化。与现有方法相比，该方法在重建精度和推理时间方面表现出更优的性能。

SuperSVG: 基于超像素的可缩放矢量图形合成

SuperSVG: Superpixel-based Scalable Vector Graphics Synthesis

In the field of computer graphics, the use of vector graphics, particularly
Scalable Vector Graphics (SVG), represents a notable development from
traditional pixel-based imagery. SVGs, with their XML-based format, are
distinct in their ability to directly and explicitly represent visual elements
such as shape, color, and path. This direct representation facilitates a more
accurate and logical depiction of graphical elements, enhancing reasoning and
interpretability. Recognizing the potential of SVGs, the machine learning
community has introduced multiple methods for image vectorization. However,
transforming images into SVG format while retaining the relational properties
and context of the original scene remains a key challenge. Most vectorization
methods often yield SVGs that are overly complex and not easily interpretable.
In response to this challenge, we introduce our method, Simple-SVG-Generation
(S\textsuperscript{2}VG\textsuperscript{2}). Our method focuses on producing
SVGs that are both accurate and simple, aligning with human readability and
understanding. With simple images, we evaluate our method with reasoning tasks
together with advanced language models, the results show a clear improvement
over previous SVG generation methods. We also conducted surveys for human
evaluation on the readability of our generated SVGs, the results also favor our
methods.

通过引入我们的方法（Simple-SVG-Generation，简称 S	extsuperscript {2} VG	extsuperscript {2}），我们专注于生成准确且简单的 SVG 图像，与人类的可读性和理解力相一致，通过与先进的语言模型一起进行推理任务的简单图像评估，结果显示与先前的 SVG 生成方法相比有明显的改进。我们还对生成的 SVG 可读性进行了人类评估的调查，结果也对我们的方法持利好态度。

超越像素：探索视觉语言模型生成简单图像的人类可读 SVG

Beyond Pixels: Exploring Human-Readable SVG Generation for Simple Images  with Vision Language Models

Image rasterization is a mature technique in computer graphics, while image
vectorization, the reverse path of rasterization, remains a major challenge.
Recent advanced deep learning-based models achieve vectorization and semantic
interpolation of vector graphs and demonstrate a better topology of generating
new figures. However, deep models cannot be easily generalized to out-of-domain
testing data. The generated SVGs also contain complex and redundant shapes that
are not quite convenient for further editing. Specifically, the crucial
layer-wise topology and fundamental semantics in images are still not well
understood and thus not fully explored. In this work, we propose Layer-wise
Image Vectorization, namely LIVE, to convert raster images to SVGs and
simultaneously maintain its image topology. LIVE can generate compact SVG forms
with layer-wise structures that are semantically consistent with human
perspective. We progressively add new bezier paths and optimize these paths
with the layer-wise framework, newly designed loss functions, and
component-wise path initialization technique. Our experiments demonstrate that
LIVE presents more plausible vectorized forms than prior works and can be
generalized to new images. With the help of this newly learned topology, LIVE
initiates human editable SVGs for both designers and other downstream
applications. Codes are made available at
this https URL

本文提出了一种名为 LIVE 的图像逐层矢量化方法，它能够将光栅图像转换为 SVG，并同时保持其图像拓扑结构，通过新的贝塞尔路径，新设计的损失函数以及逐元件路径初始化技术来优化路径，实验结果表明，LIVE 比以前的工作呈现更有说服力的矢量形式，并且可以推广到新的图像。