Improving Masked Style Transfer using Blended Partial Convolution
Artistic style transfer has long been possible with the advancements of
convolution- and transformer-based neural networks. Most algorithms apply the
artistic style transfer to the whole image, but individual users may only need
to apply a style transfer to a specific region in the image. The standard
practice is to simply mask the image after the stylization. This work shows
that this approach tends to improperly capture the style features in the region
of interest. We propose a partial-convolution-based style transfer network that
accurately applies the style features exclusively to the region of interest.
Additionally, we present network-internal blending techniques that account for
imperfections in the region selection. We show that this visually and
quantitatively improves stylization using examples from the SA-1B dataset. Code
is publicly available at https://github.com/davidmhart/StyleTransferMasked.
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Seyed Hadi Seyed
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Ayberk Cansever
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David Hart
claimed_verified
2025-08-13T07:14:01.847Z
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/avatars/c4ba9007b1e952f1e1fc906ef0acc3ba.svg
David Hart
user
incrl
Improving Masked Style Transfer using Blended Partial Convolution
Artistic style transfer has long been possible with the advancements of
convolution- and transformer-based neural networks. Most algorithms apply the
artistic style transfer to the whole image, but individual users may only need
to apply a style transfer to a specific region in the image. The standard
practice is to simply mask the image after the stylization. This work shows
that this approach tends to improperly capture the style features in the region
of interest. We propose a partial-convolution-based style transfer network that
accurately applies the style features exclusively to the region of interest.
Additionally, we present network-internal blending techniques that account for
imperfections in the region selection. We show that this visually and
quantitatively improves stylization using examples from the SA-1B dataset. Code
is publicly available at https://github.com/davidmhart/StyleTransferMasked.
A partial-convolution-based style transfer network improves stylization of specific image regions by accurately applying style features and using network-internal blending techniques.
convolution-based neural networks transformer-based neural networks partial-convolution-based style transfer network network-internal blending techniques SA-1B dataset