  Image Style Transfer (IST) is an interdisciplinary topic of computer vision
and art that continuously attracts researchers' interests. Different from
traditional Image-guided Image Style Transfer (IIST) methods that require a
style reference image as input to define the desired style, recent works start
to tackle the problem in a text-guided manner, i.e., Text-guided Image Style
Transfer (TIST). Compared to IIST, such approaches provide more flexibility
with text-specified styles, which are useful in scenarios where the style is
hard to define with reference images. Unfortunately, many TIST approaches
produce undesirable artifacts in the transferred images. To address this issue,
we present a novel method to achieve much improved style transfer based on text
guidance. Meanwhile, to offer more flexibility than IIST and TIST, our method
allows style inputs from multiple sources and modalities, enabling
MultiModality-guided Image Style Transfer (MMIST). Specifically, we realize
MMIST with a novel cross-modal GAN inversion method, which generates style
representations consistent with specified styles. Such style representations
facilitate style transfer and in principle generalize any IIST methods to
MMIST. Large-scale experiments and user studies demonstrate that our method
achieves state-of-the-art performance on TIST task. Furthermore, comprehensive
qualitative results confirm the effectiveness of our method on MMIST task and
cross-modal style interpolation.
