Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fine-Grained Control of Artistic Styles in Image Generation   Recent advances in generative models and adversarial training have enabled
artificially generating artworks in various artistic styles. It is highly
desirable to gain more control over the generated style in practice. However,
artistic styles are unlike object categories -- there are a continuous spectrum
of styles distinguished by subtle differences. Few works have been explored to
capture the continuous spectrum of styles and apply it to a style generation
task. In this paper, we propose to achieve this by embedding original artwork
examples into a continuous style space. The style vectors are fed to the
generator and discriminator to achieve fine-grained control. Our method can be
used with common generative adversarial networks (such as StyleGAN).
Experiments show that our method not only precisely controls the fine-grained
artistic style but also improves image quality over vanilla StyleGAN as
measured by FID.

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The categories of this paper are: physics
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: gr-qc
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The categories of this paper are: nucl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: econ
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The categories of this paper are: math-ph
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The categories of this paper are: acc-phys