  Despite having promising results, style transfer, which requires preparing
style images in advance, may result in lack of creativity and accessibility.
Following human instruction, on the other hand, is the most natural way to
perform artistic style transfer that can significantly improve controllability
for visual effect applications. We introduce a new task, language-driven
artistic style transfer (LDAST), to manipulate the style of a content image,
guided by a text. We propose contrastive language visual artist (CLVA) that
learns to extract visual semantics from style instructions and accomplish LDAST
by the patch-wise style discriminator. The discriminator considers the
correlation between language and patches of style images or transferred results
to jointly embed style instructions. CLVA further compares contrastive pairs of
content images and style instructions to improve the mutual relativeness. The
results from the same content image can preserve consistent content structures.
Besides, they should present analogous style patterns from style instructions
that contain similar visual semantics. The experiments show that our CLVA is
effective and achieves superb transferred results on LDAST.
