  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
https://szuviz.github.io/layered_vectorization/.
