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The study of Ukiyo-e, an important genre of pre-modern Japanese art, focuseson the object and style like other artwork researches. Such study has benefitedfrom the renewed interest by the machine learning community in culturallyimportant topics, leading to interdisciplinary works including collections ofimages, quantitative approaches, and machine learning-based creativities. They,however, have several drawbacks, and it remains challenging to integrate theseworks into a comprehensive view. To bridge this gap, we propose a holisticapproach We first present a large-scale Ukiyo-e dataset with coherent semanticlabels and geometric annotations, then show its value in a quantitative studyof Ukiyo-e paintings' object using these labels and annotations. We furtherdemonstrate the machine learning methods could help style study through softcolor decomposition of Ukiyo-e, and finally provides joint insights into objectand style by composing sketches and colors using colorization. Datasetavailable at https://github.com/rois-codh/arc-ukiyoe-faces

Ukiyo-e Analysis and Creativity with Attribute and Geometry Annotation