OregairuChar: A Benchmark Dataset for Character Appearance Frequency Analysis in My Teen Romantic Comedy SNAFU

TL;DR

OregairuChar is a new benchmark dataset with annotated frames from anime, enabling analysis of character prominence and narrative structure through appearance frequency and object detection models.

Contribution

The paper introduces OregairuChar, a comprehensive dataset for character appearance analysis in anime, along with benchmarking of detection models for narrative insights.

Findings

Object detection models achieve baseline performance on the dataset.

Character prominence patterns are identified over episodes.

The dataset captures diverse visual challenges like occlusion and pose variation.

Abstract

The analysis of character appearance frequency is essential for understanding narrative structure, character prominence, and story progression in anime. In this work, we introduce OregairuChar, a benchmark dataset designed for appearance frequency analysis in the anime series My Teen Romantic Comedy SNAFU. The dataset comprises 1600 manually selected frames from the third season, annotated with 2860 bounding boxes across 11 main characters. OregairuChar captures diverse visual challenges, including occlusion, pose variation, and inter-character similarity, providing a realistic basis for appearance-based studies. To enable quantitative research, we benchmark several object detection models on the dataset and leverage their predictions for fine-grained, episode-level analysis of character presence over time. This approach reveals patterns of character prominence and their evolution within the narrative. By emphasizing appearance frequency, OregairuChar serves as a valuable resource for exploring computational narrative dynamics and character-centric storytelling in stylized media.