Semi-Automatic Generation of Rotoscope Animation Using SAM and k-means Clustering

Mizuki Sakakibara, Tomokazu Ishikawa, Tomokazu Ishikawa

2025

Abstract

This paper proposes a novel method for automating the rotoscoping process in anime production by combining SAM (Segment Anything Model) and k-means clustering. Traditional rotoscoping, which involves manually tracing live-action footage, is time-consuming and labor-intensive. Our method automatically generates line drawings and coloring regions suitable for anime production workflows through three main steps: line drawing creation using SAM2, shadow region generation using k-means clustering, and finishing with color design. Experimental results from 134 participants showed that our method achieved significantly higher ratings in both “rotoscope-likeness” and “anime-likeness” compared to existing methods, particularly in depicting complex human movements and details. The method also enables hierarchical editing of animation materials and efficient color application across multiple frames, making it more suitable for commercial anime production pipelines than existing style transfer approaches. While the current implementation has limitations regarding segmentation accuracy and line drawing detail, it represents a significant step toward automating and streamlining the anime production process.

Paper Citation

in Harvard Style

Sakakibara M. and Ishikawa T. (2025). Semi-Automatic Generation of Rotoscope Animation Using SAM and k-means Clustering. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP; ISBN 978-989-758-728-3, SciTePress, pages 363-370. DOI: 10.5220/0013321000003912

in Bibtex Style

@conference{grapp25,
author={Mizuki Sakakibara and Tomokazu Ishikawa},
title={Semi-Automatic Generation of Rotoscope Animation Using SAM and k-means Clustering},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP},
year={2025},
pages={363-370},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013321000003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP
TI - Semi-Automatic Generation of Rotoscope Animation Using SAM and k-means Clustering
SN - 978-989-758-728-3
AU - Sakakibara M.
AU - Ishikawa T.
PY - 2025
SP - 363
EP - 370
DO - 10.5220/0013321000003912
PB - SciTePress