This work presents a novel progressive image vectorization technique aimed at
generating layered vectors that represent the original image from coarse to
fine detail levels. Our approach introduces semantic simplification, which
combines Score Distillation Sampling and semantic segmentation to iteratively
simplify the input image. Subsequently, our method optimizes the vector layers
for each of the progressively simplified images. Our method provides robust
optimization, which avoids local minima and enables adjustable detail levels in
the final output. The layered, compact vector representation enhances usability
for further editing and modification. Comparative analysis with conventional
vectorization methods demonstrates our technique's superiority in producing
vectors with high visual fidelity, and more importantly, maintaining vector
compactness and manageability. The project homepage is
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本研究提出了一种新的渐进式图像矢量化技术，旨在生成层次化的矢量，以从粗糙到细节层次逐渐表示原始图像。我们的方法引入了语义简化，结合了分数蒸馏采样和语义分割，来迭代地简化输入图像。随后，我们的方法针对每个逐步简化的图像优化矢量层。我们的方法提供了稳健的优化，避免了局部极小值，并能够调整最终输出中的细节级别。层次化、紧凑的矢量表示增强了进一步编辑和修改的可用性。与传统的矢量化方法进行对比分析显示了我们技术在产生高视觉保真度矢量以及保持矢量的紧凑性和可管理性方面的优越性。