Pose Estimation of Artwork Characters with Series and Parallel Dilated Convolution And Style Channel Attention

2D pose estimation is a fundamental task predicting geometric construction of targets like a human and an animal from input images. Notably, we focus on characters that are drawn on canvas, such as illustrations and paintings. Poses of the characters can be used for many applications, such as a retrieval system and detecting plagiarism. However, it is difficult to identify the poses because of domain characteristics. In this paper, we tackle that domain issue by developing Series and Parallel Dilated Convolution (SPDC), which is based on an inverted residual bottleneck and has cascade connections and parallel connections simultaneously. In addition, we propose Style Channel Attention (SCA), which reflects style information of an input image in estimation. These modules can deal with different painting styles and parts of characters. Compared with existing methods, our method improves average precision by 2.89 % and average recall by 2.58 % in evaluating the results with an illustration dataset.