2021/05/19 by Hiromu Yakura, Yakura, Hiromu, Yuki Koyama +3
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Human-Computer Interaction (cs.HC) #Machine Learning (cs.LG) #Music Technology and Sound Studies #Music and Audio Processing #cs.CV #cs.HC #cs.LG
paper · pdf · doi:10.48550/arxiv.2105.09207
To appear in Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI 2021); Project page available at https://yumetaro.info/projects/parametric-transcription/
arxiv created 2021/05/19 · openalex publication_date 2021/05/19 · arxiv updated 2021/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.