Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Awesome Typography: Statistics-Based Text Effects Transfer   In this work, we explore the problem of generating fantastic special-effects
for the typography. It is quite challenging due to the model diversities to
illustrate varied text effects for different characters. To address this issue,
our key idea is to exploit the analytics on the high regularity of the spatial
distribution for text effects to guide the synthesis process. Specifically, we
characterize the stylized patches by their normalized positions and the optimal
scales to depict their style elements. Our method first estimates these two
features and derives their correlation statistically. They are then converted
into soft constraints for texture transfer to accomplish adaptive multi-scale
texture synthesis and to make style element distribution uniform. It allows our
algorithm to produce artistic typography that fits for both local texture
patterns and the global spatial distribution in the example. Experimental
results demonstrate the superiority of our method for various text effects over
conventional style transfer methods. In addition, we validate the effectiveness
of our algorithm with extensive artistic typography library generation.

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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: nlin
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The categories of this paper are: physics
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The categories of this paper are: math
33
The categories of this paper are: plasm-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: eess
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The categories of this paper are: adap-org
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The categories of this paper are: mtrl-th
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The categories of this paper are: econ
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The categories of this paper are: dg-ga
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The categories of this paper are: astro-ph
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The categories of this paper are: ao-sci