SVGDreamer: Text Guided SVG Generation with Diffusion Model

• URL: http://arxiv.org/abs/2312.16476v5
• Date: Tue, 2 Apr 2024 13:25:04 GMT
• Title: SVGDreamer: Text Guided SVG Generation with Diffusion Model
• Authors: Ximing Xing, Haitao Zhou, Chuang Wang, Jing Zhang, Dong Xu, Qian Yu,
• Abstract summary: 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.
• Score: 31.76771064173087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, text-guided scalable vector graphics (SVGs) synthesis has shown promise in domains such as iconography and sketch. However, existing text-to-SVG generation methods lack editability and struggle with visual quality and result diversity. To address these limitations, we propose a novel text-guided vector graphics synthesis method called SVGDreamer. SVGDreamer incorporates a semantic-driven image vectorization (SIVE) process that enables the decomposition of synthesis into foreground objects and background, thereby enhancing editability. Specifically, the SIVE process introduces attention-based primitive control and an attention-mask loss function for effective control and manipulation of individual elements. Additionally, we propose a Vectorized Particle-based Score Distillation (VPSD) approach to address issues of shape over-smoothing, color over-saturation, limited diversity, and slow convergence of the existing text-to-SVG generation methods by modeling SVGs as distributions of control points and colors. Furthermore, VPSD leverages a reward model to re-weight vector particles, which improves aesthetic appeal and accelerates convergence. Extensive experiments are conducted to validate the effectiveness of SVGDreamer, demonstrating its superiority over baseline methods in terms of editability, visual quality, and diversity. Project page: \href{https://ximinng.github.io/SVGDreamer-project/}{https://ximinng.github.io/SVGDreamer-project/}

Related papers

• Vector Grimoire: Codebook-based Shape Generation under Raster Image Supervision [20.325246638505714]
  We introduce GRIMOIRE, a text-guided generative model that learns to map images onto a discrete codebook by reconstructing them as vector shapes. Unlike existing models that require direct supervision from data, GRIMOIRE learns using only image supervision which opens up vector generative modeling to significantly more data.
  arXiv  Detail & Related papers  (2024-10-08T12:41:31Z)
• 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)
• StarVector: Generating Scalable Vector Graphics Code from Images [13.995963187283321]
  This paper introduces Star, a multimodal SVG generation model that integrates Code Generation Large Language Models (CodeLLMs) and vision models. Our approach utilizes a CLIP image to extract visual representations from pixel-based images, which are then transformed into visual tokens via an adapter module. Our results demonstrate significant enhancements in visual quality and complexity over current methods, marking a notable advancement in SVG generation technology.
  arXiv  Detail & Related papers  (2023-12-17T08:07:32Z)
• Beyond Pixels: Exploring Human-Readable SVG Generation for Simple Images with Vision Language Models [19.145503353922038]
  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.
  arXiv  Detail & Related papers  (2023-11-27T05:20:11Z)
• 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)
• Kernel Stein Generative Modeling [68.03537693810972]
  Gradient Langevin Dynamics (SGLD) demonstrates impressive results with energy-based models on high-dimensional and complex data distributions. Stein Variational Gradient Descent (SVGD) is a deterministic sampling algorithm that iteratively transports a set of particles to approximate a given distribution. We propose noise conditional kernel SVGD (NCK-SVGD), that works in tandem with the recently introduced Noise Conditional Score Network estimator.
  arXiv  Detail & Related papers  (2020-07-06T21:26:04Z)

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.