# Ukiyo-e Analysis and Creativity with Attribute and Geometry Annotation

arXiv: https://arxiv.org/abs/2106.02267 · HTML: https://mlsanity.com/paper/2106.02267/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs](https://mlsanity.com/paper/1711.11585/) (2017)
- [Resolution Dependent GAN Interpolation for Controllable Image Synthesis Between Domains](https://mlsanity.com/paper/2010.05334/) (2020)
- [Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence](https://mlsanity.com/paper/2005.05207/) (2020)
- [KaoKore: A Pre-modern Japanese Art Facial Expression Dataset](https://mlsanity.com/paper/2002.08595/) (2020)
- [Fast Soft Color Segmentation](https://mlsanity.com/paper/2004.08096/) (2020)
- [Historical and Modern Features for Buddha Statue Classification](https://mlsanity.com/paper/1909.12921/) (2019)

## Built on by (most cited first, 2 of 2)

- [Stylistic Multi-Task Analysis of Ukiyo-e Woodblock Prints](https://mlsanity.com/paper/2410.12379/) (2024)
- [Disruptive Transformation of Artworks in Master-Disciple Relationships: The Case of Ukiyo-e Artworks](https://mlsanity.com/paper/2505.08284/) (2025)

## Abstract (arXiv)

The study of Ukiyo-e, an important genre of pre-modern Japanese art, focuses on the object and style like other artwork researches. Such study has benefited from the renewed interest by the machine learning community in culturally important topics, leading to interdisciplinary works including collections of images, quantitative approaches, and machine learning-based creativities. They, however, have several drawbacks, and it remains challenging to integrate these works into a comprehensive view. To bridge this gap, we propose a holistic approach We first present a large-scale Ukiyo-e dataset with coherent semantic labels and geometric annotations, then show its value in a quantitative study of Ukiyo-e paintings' object using these labels and annotations. We further demonstrate the machine learning methods could help style study through soft color decomposition of Ukiyo-e, and finally provides joint insights into object and style by composing sketches and colors using colorization. Dataset available at https://github.com/rois-codh/arc-ukiyoe-faces