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.

通过将端到端的样式转移效果转录为现有内容编辑工具中的特定转换的参数值，本文提出了参数化转录方法，以便于用户可以使用他们熟悉的工具模仿参考样本的样式，并通过操纵参数继续进一步的探索设计。利用现有的预训练模型计算与参考样本的感知风格距离以及使用黑盒优化来找到最小化该距离的参数，我们的实验显示，本框架可以有效地利用深度学习技术来支持计算设计。

利用预先训练的知觉度量参数化风格转移效果的工具和领域不可知化