title: VecFusion: Vector Font Generation with Diffusion
summary: We present VecFusion, a new neural architecture that can generate vector
  fonts with varying topological structures and precise control point positions.
  Our approach is a cascaded diffusion model which consists of a raster diffusion
  model followed by a vector diffusion model. The raster model generates
  low-resolution, rasterized fonts with auxiliary control point information,
  capturing the global style and shape of the font, while the vector model
  synthesizes vector fonts conditioned on the low-resolution raster fonts from
  the first stage. To synthesize long and complex curves, our vector diffusion
  model uses a transformer architecture and a novel vector representation that
  enables the modeling of diverse vector geometry and the precise prediction of
  control points. Our experiments show that, in contrast to previous generative
  models for vector graphics, our new cascaded vector diffusion model generates
  higher quality vector fonts, with complex structures and diverse styles.
paper_authors._id: 65813908208369a94032a60a
paper_authors.name: Vikas Thamizharasan
paper_authors.status: admin_assigned
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paper_authors.fullname: Vikas Thamizharasan
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paper_authors.name: Difan Liu
paper_authors.status: admin_assigned
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paper_authors.fullname: Difan Liu
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paper_authors.user: smebliu
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paper_authors.name: Shantanu Agarwal
paper_authors._id: 65813908208369a94032a60d
paper_authors.name: Matthew Fisher
paper_authors._id: 65813908208369a94032a60e
paper_authors.name: Michael Gharbi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-12-19T09:30:10.297Z
paper_authors._id: 658161ad208369a9403ca9a1
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paper_authors.fullname: Michael Gharbi
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paper_authors.user: mgharbi
paper_authors._id: 65813908208369a94032a60f
paper_authors.name: Oliver Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-19T08:59:49.816Z
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paper_authors.avatarUrl: /avatars/71fe9cff4ce79f6a208045984852e5ad.svg
paper_authors.fullname: Oliver Wang
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paper_authors.user: oliverwang
paper_authors._id: 65813908208369a94032a610
paper_authors.name: Alec Jacobson
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-19T08:59:57.217Z
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paper_authors.fullname: Alec
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paper_authors.user: alecjacobson
paper_authors._id: 65813908208369a94032a611
paper_authors.name: Evangelos Kalogerakis
paper_title: VecFusion: Vector Font Generation with Diffusion
paper_summary: We present VecFusion, a new neural architecture that can generate vector
  fonts with varying topological structures and precise control point positions.
  Our approach is a cascaded diffusion model which consists of a raster diffusion
  model followed by a vector diffusion model. The raster model generates
  low-resolution, rasterized fonts with auxiliary control point information,
  capturing the global style and shape of the font, while the vector model
  synthesizes vector fonts conditioned on the low-resolution raster fonts from
  the first stage. To synthesize long and complex curves, our vector diffusion
  model uses a transformer architecture and a novel vector representation that
  enables the modeling of diverse vector geometry and the precise prediction of
  control points. Our experiments show that, in contrast to previous generative
  models for vector graphics, our new cascaded vector diffusion model generates
  higher quality vector fonts, with complex structures and diverse styles.
paper_ai_summary: A cascaded diffusion model combines raster and vector diffusion models, using a transformer architecture and novel vector representation to generate high-quality, complex vector fonts.
paper_ai_keywords: neural architecture VecFusion cascaded diffusion model raster diffusion model vector diffusion model transformer architecture vector representation vector fonts control points