VecFusion: Vector Font Generation with Diffusion
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.
65813908208369a94032a60a
Vikas Thamizharasan
admin_assigned
2023-12-19T11:34:16.914Z
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/avatars/054f960cc875351604a49ec4dc738bc1.svg
Vikas Thamizharasan
user
sm00th-manif0ld
65813908208369a94032a60b
Difan Liu
admin_assigned
2023-12-19T08:58:16.214Z
6393a754d31aec207924611b
/avatars/51afe808f8e8a032e34ae6d7a2d6e407.svg
Difan Liu
user
smebliu
65813908208369a94032a60c
Shantanu Agarwal
65813908208369a94032a60d
Matthew Fisher
65813908208369a94032a60e
Michael Gharbi
claimed_verified
2023-12-19T09:30:10.297Z
658161ad208369a9403ca9a1
/avatars/e2324ab2a712100681b0b8127305212b.svg
Michael Gharbi
user
mgharbi
65813908208369a94032a60f
Oliver Wang
admin_assigned
2023-12-19T08:59:49.816Z
62fa8bfdc95d426ff8f25fe9
/avatars/71fe9cff4ce79f6a208045984852e5ad.svg
Oliver Wang
user
oliverwang
65813908208369a94032a610
Alec Jacobson
admin_assigned
2023-12-19T08:59:57.217Z
63321730fece0490c3d0dca0
/avatars/552d91d82969b8f227210655d790ee1e.svg
Alec
user
alecjacobson
65813908208369a94032a611
Evangelos Kalogerakis
VecFusion: Vector Font Generation with Diffusion
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.
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.
neural architecture VecFusion cascaded diffusion model raster diffusion model vector diffusion model transformer architecture vector representation vector fonts control points