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.
翻译:人类造型信息是许多下游图像处理任务的关键组成部分,例如活动识别和运动跟踪。同样,图示字符域的构成估计器将为辅助性内容创建任务提供宝贵的前程,例如参考显示检索和自动字符动画。但是,虽然现代数据驱动技术大大改进了自然图像的估计性能,但对插图却没有做多少工作。在我们的工作中,我们通过从特定领域和特定任务源模型有效传输学习来弥合这个领域的差距。此外,我们提升和扩大现有的图示显示的构成估计数据集,并为分类和分解子任务引入两个新的数据集。然后,我们运用由此产生的最先进的性能估计器来解决图示图解检索的新任务。所有数据、模型和代码都将公布于众。