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

• URL: http://arxiv.org/abs/2311.15543v1
• Date: Mon, 27 Nov 2023 05:20:11 GMT
• Title: Beyond Pixels: Exploring Human-Readable SVG Generation for Simple Images with Vision Language Models
• Authors: Tong Zhang, Haoyang Liu, Peiyan Zhang, Yuxuan Cheng, Haohan Wang
• Abstract summary: We introduce our method, Simple-SVG-Generation (Stextsuperscript2VGtextsuperscript2). 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.
• Score: 19.145503353922038
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• NeuralSVG: An Implicit Representation for Text-to-Vector Generation [54.4153300455889]
  We propose NeuralSVG, an implicit neural representation for generating vector graphics from text prompts. To encourage a layered structure in the generated SVG, we introduce a dropout-based regularization technique. We demonstrate that NeuralSVG outperforms existing methods in generating structured and flexible SVG.
  arXiv  Detail & Related papers  (2025-01-07T18:50:06Z)
• SVGBuilder: Component-Based Colored SVG Generation with Text-Guided Autoregressive Transformers [5.921625661186367]
  This paper introduces a component-based, autoregressive model for generating high-quality colored SVGs from textual input. It significantly reduces computational overhead and improves efficiency compared to traditional methods. To address the limitations of existing SVG datasets and support our research, we introduce ColorSVG-100K, the first large-scale dataset of colored SVGs.
  arXiv  Detail & Related papers  (2024-12-13T15:24:11Z)
• Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Models [14.917583676464266]
  Chat2SVG is a hybrid framework that combines Large Language Models and image diffusion models for text-to-SVG generation. Our system enables intuitive editing through natural language instructions, making professional vector graphics creation accessible to all users.
  arXiv  Detail & Related papers  (2024-11-25T17:31:57Z)
• 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)
• SVGDreamer: Text Guided SVG Generation with Diffusion Model [31.76771064173087]
  We propose a novel text-guided vector graphics synthesis method called SVGDreamer. SIVE process enables decomposition of synthesis into foreground objects and background. VPSD approach addresses issues of shape over-smoothing, color over-saturation, limited diversity, and slow convergence.
  arXiv  Detail & Related papers  (2023-12-27T08:50:01Z)
• StarVector: Generating Scalable Vector Graphics Code from Images and Text [15.32194071443065]
  We introduce Star, a multimodal large language model for SVG generation. It performs image vectorization by understanding image semantics and using SVG primitives for compact, precise outputs. We train StarStack, a diverse dataset of 2M samples that enables generalization across vectorization tasks.
  arXiv  Detail & Related papers  (2023-12-17T08:07:32Z)
• VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models [82.93345261434943]
  We show that a text-conditioned diffusion model trained on pixel representations of images can be used to generate SVG-exportable vector graphics. Inspired by recent text-to-3D work, we learn an SVG consistent with a caption using Score Distillation Sampling. Experiments show greater quality than prior work, and demonstrate a range of styles including pixel art and sketches.
  arXiv  Detail & Related papers  (2022-11-21T10:04:27Z)
• 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)
• SVG-Net: An SVG-based Trajectory Prediction Model [67.68864911674308]
  Anticipating motions of vehicles in a scene is an essential problem for safe autonomous driving systems. To this end, the comprehension of the scene's infrastructure is often the main clue for predicting future trajectories. Most of the proposed approaches represent the scene with averse averseized format and some of the more recent approaches leverage custom vectorized formats.
  arXiv  Detail & Related papers  (2021-10-07T18:00:08Z)
• DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation [217.86315551526235]
  We propose a novel hierarchical generative network, called DeepSVG, for complex SVG icons generation and manipulation. Our architecture effectively disentangles high-level shapes from the low-level commands that encode the shape itself. We demonstrate that our network learns to accurately reconstruct diverse vector graphics, and can serve as a powerful animation tool.
  arXiv  Detail & Related papers  (2020-07-22T09:36:31Z)

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.