Content and Style Disentanglement for Artistic Style Transfer
| Autor: | Artsiom Sanakoyeu, Sabine Lang, Björn Ommer, Dmytro Kotovenko |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry media_common.quotation_subject 02 engineering and technology 010501 environmental sciences 01 natural sciences Object (philosophy) Tone (literature) Style (sociolinguistics) Task (project management) 0202 electrical engineering electronic engineering information engineering Task analysis 020201 artificial intelligence & image processing Quality (business) Artificial intelligence business Period (music) 0105 earth and related environmental sciences media_common Cognitive psychology |
| Zdroj: | ICCV |
| Popis: | Artists rarely paint in a single style throughout their career. More often they change styles or develop variations of it. In addition, artworks in different styles and even within one style depict real content differently: while Picasso's Blue Period displays a vase in a blueish tone but as a whole, his Cubist works deconstruct the object. To produce artistically convincing stylizations, style transfer models must be able to reflect these changes and variations. Recently many works have aimed to improve the style transfer task, but neglected to address the described observations. We present a novel approach which captures particularities of style and the variations within and separates style and content. This is achieved by introducing two novel losses: a fixpoint triplet style loss to learn subtle variations within one style or between different styles and a disentanglement loss to ensure that the stylization is not conditioned on the real input photo. In addition the paper proposes various evaluation methods to measure the importance of both losses on the validity, quality and variability of final stylizations. We provide qualitative results to demonstrate the performance of our approach. |
| Databáze: | OpenAIRE |
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