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Current deep learning techniques for style transfer would not be optimal fordesign support since their "one-shot" transfer does not fit exploratory designprocesses. To overcome this gap, we propose parametric transcription, whichtranscribes an end-to-end style transfer effect into parameter values ofspecific transformations available in an existing content editing tool. Withthis approach, users can imitate the style of a reference sample in the toolthat they are familiar with and thus can easily continue further exploration bymanipulating the parameters. To enable this, we introduce a framework thatutilizes an existing pretrained model for style transfer to calculate aperceptual style distance to the reference sample and uses black-boxoptimization to find the parameters that minimize this distance. Ourexperiments with various third-party tools, such as Instagram and Blender, showthat our framework can effectively leverage deep learning techniques forcomputational design support.

Tool- and Domain-Agnostic Parameterization of Style Transfer Effects Leveraging Pretrained Perceptual Metrics