  Current deep learning techniques for style transfer would not be optimal for
design support since their "one-shot" transfer does not fit exploratory design
processes. To overcome this gap, we propose parametric transcription, which
transcribes an end-to-end style transfer effect into parameter values of
specific transformations available in an existing content editing tool. With
this approach, users can imitate the style of a reference sample in the tool
that they are familiar with and thus can easily continue further exploration by
manipulating the parameters. To enable this, we introduce a framework that
utilizes an existing pretrained model for style transfer to calculate a
perceptual style distance to the reference sample and uses black-box
optimization to find the parameters that minimize this distance. Our
experiments with various third-party tools, such as Instagram and Blender, show
that our framework can effectively leverage deep learning techniques for
computational design support.
