Skeleton2Stroke: Interactive Stroke Correspondence Editing with Pose Features

Abstract

Inbetweening is an important technique for computer animations where the stroke correspondence of hand-drawn illustrations plays a significant role. Previous works typically require image vectorization and enormous computation cost to achieve this goal. In this paper, we propose an interactive method to construct stroke correspondences in character illustrations. First, we utilize a deep learning-based skeleton estimation to improve the accuracy of closed-area correspondences, which are obtained using greedy algorithm. Second, we construct stroke correspondences based on the estimated closed-area correspondences. The proposed user interface is verified by our experiment to ensure that the users can achieve high accuracy with low correction in stroke correspondence.