# Unsupervised Manga Character Re-identification via Face-body and Spatial-temporal Associated Clustering

arXiv: https://arxiv.org/abs/2204.04621 · HTML: https://mlsanity.com/paper/2204.04621/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 10 Apr 2022.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection](https://mlsanity.com/paper/2005.09007/) (2020)
- [Sketch-based Manga Retrieval using Manga109 Dataset](https://mlsanity.com/paper/1510.04389/) (2015)
- [Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification](https://mlsanity.com/paper/2001.01526/) (2020)
- [Joint Detection and Identification Feature Learning for Person Search](https://mlsanity.com/paper/1604.01850/) (2016)
- [Unsupervised Person Re-identification via Softened Similarity Learning](https://mlsanity.com/paper/2004.03547/) (2020)
- [ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.07698/) (2018)
- [When Person Re-identification Meets Changing Clothes](https://mlsanity.com/paper/2003.04070/) (2020)
- [The Amazing Mysteries of the Gutter: Drawing Inferences Between Panels in Comic Book Narratives](https://mlsanity.com/paper/1611.05118/) (2016)
- [Towards Fully Automated Manga Translation](https://mlsanity.com/paper/2012.14271/) (2020)
- [Object Detection for Comics using Manga109 Annotations](https://mlsanity.com/paper/1803.08670/) (2018)
- [MangaGAN: Unpaired Photo-to-Manga Translation Based on The Methodology of Manga Drawing](https://mlsanity.com/paper/2004.10634/) (2020)
- [DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition](https://mlsanity.com/paper/2101.08674/) (2021)

## Built on by (most cited first, 9 of 9)

- [The Manga Whisperer: Automatically Generating Transcriptions for Comics](https://mlsanity.com/paper/2401.10224/) (2024)
- [Tails Tell Tales: Chapter-Wide Manga Transcriptions with Character Names](https://mlsanity.com/paper/2408.00298/) (2024)
- [Zero-Shot Character Identification and Speaker Prediction in Comics via Iterative Multimodal Fusion](https://mlsanity.com/paper/2404.13993/) (2024)
- [Identity-Aware Semi-Supervised Learning for Comic Character Re-Identification](https://mlsanity.com/paper/2308.09096/) (2023)
- [One missing piece in Vision and Language: A Survey on Comics Understanding](https://mlsanity.com/paper/2409.09502/) (2024)
- [From Panels to Prose: Generating Literary Narratives from Comics](https://mlsanity.com/paper/2503.23344/) (2025)
- [Toward accessible comics for blind and low vision readers](https://mlsanity.com/paper/2407.08248/) (2024)
- [Emotion-Aware Speech Generation with Character-Specific Voices for Comics](https://mlsanity.com/paper/2509.15253/) (2025)
- [Re:Cognize -- Open-Set Comic Character Re-Identification](https://mlsanity.com/paper/2609.34032/) (2026)

## Abstract (arXiv)

In the past few years, there has been a dramatic growth in e-manga (electronic Japanese-style comics). Faced with the booming demand for manga research and the large amount of unlabeled manga data, we raised a new task, called unsupervised manga character re-identification. However, the artistic expression and stylistic limitations of manga pose many challenges to the re-identification problem. Inspired by the idea that some content-related features may help clustering, we propose a Face-body and Spatial-temporal Associated Clustering method (FSAC). In the face-body combination module, a face-body graph is constructed to solve problems such as exaggeration and deformation in artistic creation by using the integrity of the image. In the spatial-temporal relationship correction module, we analyze the appearance features of characters and design a temporal-spatial-related triplet loss to fine-tune the clustering. Extensive experiments on a manga book dataset with 109 volumes validate the superiority of our method in unsupervised manga character re-identification.