Layered Image Vectorization via Semantic Simplification

• URL: http://arxiv.org/abs/2406.05404v1
• Date: Sat, 8 Jun 2024 08:54:35 GMT
• Title: Layered Image Vectorization via Semantic Simplification
• Authors: Zhenyu Wang, Jianxi Huang, Zhida Sun, Daniel Cohen-Or, Min Lu,
• Abstract summary: 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. Our method provides robust optimization, which avoids local minima and enables adjustable detail levels in the final output.
• Score: 46.23779847614095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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/.

Related papers

• DeepIcon: A Hierarchical Network for Layer-wise Icon Vectorization [12.82009632507056]
  Recent learning-based methods for converting images to vector formats frequently suffer from incomplete shapes, redundant path prediction, and a lack of accuracy in preserving the semantics of the original content. We present DeepIcon, a novel hierarchical image vectorization network specifically tailored generating variable-length icon graphics based on the image input.
  arXiv  Detail & Related papers  (2024-10-21T08:20:19Z)
• Segmentation-guided Layer-wise Image Vectorization with Gradient Fills [6.037332707968933]
  We propose a segmentation-guided vectorization framework to convert images into concise vector graphics with gradient fills. With the guidance of an embedded gradient-aware segmentation, our approach progressively appends gradient-filled B'ezier paths to the output.
  arXiv  Detail & Related papers  (2024-08-28T12:08:25Z)
• SuperSVG: Superpixel-based Scalable Vector Graphics Synthesis [66.44553285020066]
  SuperSVG is a superpixel-based vectorization model that achieves fast and high-precision image vectorization. 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. Experiments demonstrate the superior performance of our method in terms of reconstruction accuracy and inference time compared to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-06-14T07:43:23Z)
• VectorPainter: A Novel Approach to Stylized Vector Graphics Synthesis with Vectorized Strokes [17.222592006593057]
  We propose a novel method, VectorPainter, for the task of stylized vector graphics synthesis. Given a text prompt and a reference style image, VectorPainter generates a vector graphic that aligns in content with the text prompt and remains faithful in style to the reference image.
  arXiv  Detail & Related papers  (2024-05-05T15:01:29Z)
• Texture, Shape and Order Matter: A New Transformer Design for Sequential DeepFake Detection [57.100891917805086]
  Sequential DeepFake detection is an emerging task that predicts the manipulation sequence in order. This paper describes a new Transformer design, called TSOM, by exploring three perspectives: Texture, Shape, and Order of Manipulations.
  arXiv  Detail & Related papers  (2024-04-22T04:47:52Z)
• StrokeNUWA: Tokenizing Strokes for Vector Graphic Synthesis [112.25071764647683]
  StrokeNUWA is a pioneering work exploring a better visual representation ''stroke tokens'' on vector graphics. equipped with stroke tokens, StrokeNUWA can significantly surpass traditional LLM-based and optimization-based methods. StrokeNUWA achieves up to a 94x speedup in inference over the speed of prior methods with an exceptional SVG code compression ratio of 6.9%.
  arXiv  Detail & Related papers  (2024-01-30T15:20:26Z)
• Optimize and Reduce: A Top-Down Approach for Image Vectorization [12.998637003026273]
  We propose Optimize & Reduce (O&R), a top-down approach to vectorization that is both fast and domain-agnostic. O&R aims to attain a compact representation of input images by iteratively optimizing B'ezier curve parameters. We demonstrate that our method is domain agnostic and outperforms existing works in both reconstruction and perceptual quality for a fixed number of shapes.
  arXiv  Detail & Related papers  (2023-12-18T16:41:03Z)
• Improving Image Tracing with Convolutional Autoencoders by High-Pass Filter Preprocessing [0.6367279911825442]
  This study looks into several processing methods that include high-pass filtering, autoencoding, and vectorization to extract an abstract representation of an image. According to the findings, rebuilding an image with autoencoders, high-pass filtering it, and then vectorizing it can represent the image more abstractly while increasing the effectiveness of the vectorization process.
  arXiv  Detail & Related papers  (2023-06-15T10:59:29Z)
• Towards Layer-wise Image Vectorization [57.26058135389497]
  We propose Layerwise Image Vectorization, namely LIVE, to convert images to SVGs and simultaneously maintain its image topology. Live generates compact forms with layer-wise structures that are semantically consistent with human perspective. Live initiates human editable SVGs for both designers and can be used in other applications.
  arXiv  Detail & Related papers  (2022-06-09T17:55:02Z)
• Progressive Encoding for Neural Optimization [92.55503085245304]
  We show the competence of the PPE layer for mesh transfer and its advantages compared to contemporary surface mapping techniques. Most importantly, our technique is a parameterization-free method, and thus applicable to a variety of target shape representations.
  arXiv  Detail & Related papers  (2021-04-19T08:22:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.