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SVG360: Editable Multiview Vector Graphics from a Single SVG

arXiv 2511.16766 ↗First posted 20 Nov 2025cs.CVcited by 0

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Abstract (arXiv)

Scalable Vector Graphics are a standard representation for editable visual design, yet they are usually authored as single view two dimensional illustrations. This limits their use in applications that require object level assets to remain coherent when observed, edited, or animated from different viewpoints. We present SVG360, a framework that converts a single input SVG into geometrically and visually consistent multiview SVG assets. The key challenge is that direct per view generation or vectorization produces view dependent regions, fragmented paths, and unstable colors, making the resulting SVGs difficult to edit as a coherent object. SVG360 addresses this problem through a view consistent vectorization pipeline. It first lifts the rasterized input into a view conditioned object representation and renders target views under prescribed cameras. It then propagates part identity across neighboring views using a spatial memory mechanism adapted from video segmentation, establishing consistent region decomposition, path correspondence, and color assignment without task specific retraining. Finally, each view is reconstructed as an editable SVG through structure aware vectorization, where redundant paths are consolidated and local geometry is optimized while preserving boundaries and semantic parts. Experiments on object level SVG assets show that SVG360 improves multiview consistency, reduces path redundancy, and better preserves fine structures compared with direct per view vectorization. By turning a single view SVG into a coherent 360 degree vector asset, SVG360 expands vector graphics from static illustration toward editable multiview content for design, animation, and structured visual editing.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 25 of its 51 references that are in the map. First posted on arXiv 20 Nov 2025.

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25 of 51 references.

References · 25

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  12. 2023SyncDreamer: Generating Multiview-consistent Images from a Single-view Imagecited by 645
13 more
  1. 2023Zero123++: a Single Image to Consistent Multi-view Diffusion Base Modelcited by 521
  2. 2022GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Imagescited by 446
  3. 2023Shap-E: Generating Conditional 3D Implicit Functionscited by 385
  4. 2024SV3D: Novel Multi-view Synthesis and 3D Generation from a Single Image using Latent Video Diffusioncited by 364
  5. 2020PolyGen: An Autoregressive Generative Model of 3D Meshescited by 269
  6. 2023MeshDiffusion: Score-based Generative 3D Mesh Modelingcited by 199
  7. 2020DeepSVG: A Hierarchical Generative Network for Vector Graphics Animationcited by 159
  8. 2024Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Imagecited by 135
  9. 2021Im2Vec: Synthesizing Vector Graphics without Vector Supervisioncited by 116
  10. 2025Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridgingcited by 99
  11. 2025Sparc3D: Sparse Representation and Construction for High-Resolution 3D Shapes Modelingcited by 89
  12. 20243Doodle: Compact Abstraction of Objects with 3D Strokescited by 21
  13. 2025ViewCraft3D: High-Fidelity and View-Consistent 3D Vector Graphics Synthesiscited by 5

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