LiveSVG: Zero-Shot SVG Animation via Video Generation

• URL: http://arxiv.org/abs/2605.30174v1
• Date: Thu, 28 May 2026 16:23:34 GMT
• Title: LiveSVG: Zero-Shot SVG Animation via Video Generation
• Abstract summary: We introduce LiveSVG, a zero-shot approach for generating SVG animations using video diffusion models.<n>LiveSVG fits vector geometry directly to an explicitly generated target video.<n>We also present ChallengeSVG, a benchmark of complex, multi-object scenes.
• Score: 39.027608041126484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce LiveSVG, a zero-shot approach for generating Scalable Vector Graphics (SVG) animations using video diffusion models. Current SVG animation methods struggle with complex motions: LLM-based code synthesis fails to express fine, non-rigid Bézier deformations, while Score Distillation Sampling (SDS) provides noisy gradients and often requires category-specific priors like skeletons. In contrast, LiveSVG fits vector geometry directly to an explicitly generated target video. Given an input SVG image and a motion prompt, we generate a previewable target video using a frozen image-to-video model, then fit the original SVG to this video via differentiable rendering. Our fitting stage is skeleton-free, utilizing a dual-level motion representation that combines per-group homographies for coarse articulation with per-path Bézier control-point offsets for local deformations. To resolve color-induced correspondence ambiguities during pixel-wise fitting, we introduce a novel sphere-packing recolorization strategy. We also present ChallengeSVG, a benchmark of complex, multi-object scenes that exposes the limitations of prior work. Evaluations demonstrate that LiveSVG significantly outperforms existing methods on both AniClipart and ChallengeSVG, establishing direct reference-video fitting as a practical, robust route to prompt-aligned and fully editable vector animation.

Related papers

• VAnim: Rendering-Aware Sparse State Modeling for Structure-Preserving Vector Animation [32.226052229379086]
  We present VAnim, the first framework for open-domain text-to-SVG animation.<n>We reconceptualize animation as Sparse State Updates (SSU) on a persistent SVG DOM tree.<n>We also introduce SVGAnim-134k, the first benchmark for vector animation.
  arXiv  Detail & Related papers  (2026-05-02T16:10:55Z)
• Vector Prism: Animating Vector Graphics by Stratifying Semantic Structure [57.89872230703339]
  We introduce a framework that recovers the semantic structure required for reliable SVG animation.<n>By reorganizing SVGs into semantic groups, our approach enables VLMs to produce animations with far greater coherence.
  arXiv  Detail & Related papers  (2025-12-16T12:03:46Z)
• DuetSVG: Unified Multimodal SVG Generation with Internal Visual Guidance [48.98604326855894]
  We introduce DuetSVG, a unified multimodal model that jointly generates image tokens and corresponding SVG tokens in an end-to-end manner.<n>At inference, we apply a novel test-time scaling strategy that leverages the model's native visual predictions as guidance to improve SVG decoding quality.
  arXiv  Detail & Related papers  (2025-12-11T18:23:03Z)
• SVG360: Multi-View SVG Generation with Geometric and Color Consistency from a Single SVG [1.558477573183958]
  We present a three-stage framework that produces multi-view consistent SVGs from a single SVG input.<n>The resulting SVGs exhibit strong color consistency across views, significantly reduce redundant paths, and retain fine structural details.<n>This work bridges generative modeling and structured vector representation, providing a scalable route to single input and object level multi-view generation.
  arXiv  Detail & Related papers  (2025-11-20T19:33:57Z)
• SVGDreamer: Text Guided SVG Generation with Diffusion Model [31.76771064173087]
  We propose a novel text-guided vector graphics synthesis method called SVGDreamer.<n>SIVE process enables decomposition of synthesis into foreground objects and background.<n>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)
• 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)
• 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.