Transfer Learning for Pose Estimation of Illustrated Characters

• URL: http://arxiv.org/abs/2108.01819v1
• Date: Wed, 4 Aug 2021 02:37:28 GMT
• Title: Transfer Learning for Pose Estimation of Illustrated Characters
• Authors: Shuhong Chen, Matthias Zwicker
• Abstract summary: A pose estimator for the illustrated character domain would provide a valuable prior for assistive content creation tasks. We bridge this domain gap by efficiently transfer-learning from both domain-specific and task-specific source models. We apply the resultant state-of-the-art character pose estimator to solve the novel task of pose-guided illustration retrieval.
• Score: 37.04208600867858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human pose information is a critical component in many downstream image processing tasks, such as activity recognition and motion tracking. Likewise, a pose estimator for the illustrated character domain would provide a valuable prior for assistive content creation tasks, such as reference pose retrieval and automatic character animation. But while modern data-driven techniques have substantially improved pose estimation performance on natural images, little work has been done for illustrations. In our work, we bridge this domain gap by efficiently transfer-learning from both domain-specific and task-specific source models. Additionally, we upgrade and expand an existing illustrated pose estimation dataset, and introduce two new datasets for classification and segmentation subtasks. We then apply the resultant state-of-the-art character pose estimator to solve the novel task of pose-guided illustration retrieval. All data, models, and code will be made publicly available.

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