@inproceedings{
qiao2024capnet,
title={{CAPN}et: Cartoon Animal Parsing with Spatial Learning and Structural Modeling},
author={Jian-Jun Qiao and Meng-Yu Duan and Xiao Wu and Wei Li},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=Ibyok7prlJ}
}
Cartoon animal parsing aims to segment the body parts such as heads, arms, legs and tails from cartoon animals. Different from previous parsing tasks, cartoon animal parsing faces new challenges, including irregular body structures, abstract drawing styles and diverse animal categories. Existing methods have difficulties when addressing these challenges caused by the spatial and structural properties of cartoon animals. To address these challenges, a novel spatial learning and structural modeling network, named CAPNet, is proposed for cartoon animal parsing. It aims to address the critical problems of spatial perception, structure modeling and spatial-structural consistency learning. A spatial-aware learning module integrates deformable convolutions to learn spatial features of diverse cartoon animals. The multi-task edge and center point prediction mechanism is incorporated to capture the intricate spatial patterns. A structural modeling method is proposed to model the complex structural representations of cartoon animals, which integrates a graph neural network with a shape-aware relation learning module. To mitigate the significant differences among animals, a spatial and structural consistency learning strategy is proposed to capture and learn feature correlations across different animal species. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of the proposed approach, which outperforms state-of-the-art methods.
CAPNet: Cartoon Animal Parsing with Spatial Learning and Structural Modeling
Jian-Jun Qiao Meng-Yu Duan Xiao Wu Wei Li