Segmentation-guided Layer-wise Image Vectorization with Gradient Fills

• URL: http://arxiv.org/abs/2408.15741v1
• Date: Wed, 28 Aug 2024 12:08:25 GMT
• Title: Segmentation-guided Layer-wise Image Vectorization with Gradient Fills
• Authors: Hengyu Zhou, Hui Zhang, Bin Wang,
• Abstract summary: 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.
• Score: 6.037332707968933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The widespread use of vector graphics creates a significant demand for vectorization methods. While recent learning-based techniques have shown their capability to create vector images of clear topology, filling these primitives with gradients remains a challenge. In this paper, we propose a segmentation-guided vectorization framework to convert raster images into concise vector graphics with radial gradient fills. With the guidance of an embedded gradient-aware segmentation subroutine, our approach progressively appends gradient-filled B\'ezier paths to the output, where primitive parameters are initiated with our newly designed initialization technique and are optimized to minimize our novel loss function. We build our method on a differentiable renderer with traditional segmentation algorithms to develop it as a model-free tool for raster-to-vector conversion. It is tested on various inputs to demonstrate its feasibility, independent of datasets, to synthesize vector graphics with improved visual quality and layer-wise topology compared to prior work.

Related papers

• 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)
• Layered Image Vectorization via Semantic Simplification [46.23779847614095]
  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.
  arXiv  Detail & Related papers  (2024-06-08T08:54:35Z)
• NIVeL: Neural Implicit Vector Layers for Text-to-Vector Generation [27.22029199085009]
  NIVeL reinterprets the problem on an alternative, intermediate domain which preserves the desirable properties of vector graphics. Based on our experiments, NIVeL produces text-to-vector graphics results of significantly better quality than the state-of-the-art.
  arXiv  Detail & Related papers  (2024-05-24T05:15:45Z)
• Text-to-Vector Generation with Neural Path Representation [27.949704002538944]
  We propose a novel neural path representation that learns the path latent space from both sequence and image modalities. In the first stage, a pre-trained text-to-image diffusion model guides the initial generation of complex vector graphics. In the second stage, we refine the graphics using a layer-wise image vectorization strategy to achieve clearer elements and structure.
  arXiv  Detail & Related papers  (2024-05-16T17:59:22Z)
• 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)
• Polygonizer: An auto-regressive building delineator [12.693238093510072]
  We present an Image-to-Sequence model that allows for direct shape inference and is ready for vector-based out of the box. We demonstrate the model's performance in various ways, including perturbations to the image input that correspond to variations or artifacts commonly encountered in remote sensing applications.
  arXiv  Detail & Related papers  (2023-04-08T15:36:48Z)
• 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)
• Cloud2Curve: Generation and Vectorization of Parametric Sketches [109.02932608241227]
  We present Cloud2Curve, a generative model for scalable high-resolution vector sketches. We evaluate the generation and vectorization capabilities of our model on Quick, Draw! and KMNIST datasets.
  arXiv  Detail & Related papers  (2021-03-29T12:09:42Z)
• Channel-Directed Gradients for Optimization of Convolutional Neural Networks [50.34913837546743]
  We introduce optimization methods for convolutional neural networks that can be used to improve existing gradient-based optimization in terms of generalization error. We show that defining the gradients along the output channel direction leads to a performance boost, while other directions can be detrimental.
  arXiv  Detail & Related papers  (2020-08-25T00:44:09Z)
• Cogradient Descent for Bilinear Optimization [124.45816011848096]
  We introduce a Cogradient Descent algorithm (CoGD) to address the bilinear problem. We solve one variable by considering its coupling relationship with the other, leading to a synchronous gradient descent. Our algorithm is applied to solve problems with one variable under the sparsity constraint.
  arXiv  Detail & Related papers  (2020-06-16T13:41:54Z)

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.