Tool- and Domain-Agnostic Parameterization of Style Transfer Effects Leveraging Pretrained Perceptual Metrics
Tool- and Domain-Agnostic Parameterization of Style Transfer Effects Leveraging Pretrained Perceptual Metrics
Hiromu Yakura, Yuki Koyama, Masataka Goto
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Main Track. Pages 1208-1216.
https://doi.org/10.24963/ijcai.2021/167
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.
Keywords:
Computer Vision: 2D and 3D Computer Vision
Humans and AI: Human-AI Collaboration
Humans and AI: Intelligent User Interfaces