Instance-guided Cartoon Editing with a Large-scale Dataset
Jian Lin Chengze Li Xueting Liu Zhongping Ge
Cartoon editing, appreciated by both professional illustrators and hobbyists,
allows extensive creative freedom and the development of original narratives
within the cartoon domain. However, the existing literature on cartoon editing
is complex and leans heavily on manual operations, owing to the challenge of
automatic identification of individual character instances. Therefore, an
automated segmentation of these elements becomes imperative to facilitate a
variety of cartoon editing applications such as visual style editing, motion
decomposition and transfer, and the computation of stereoscopic depths for an
enriched visual experience. Unfortunately, most current segmentation methods
are designed for natural photographs, failing to recognize from the intricate
aesthetics of cartoon subjects, thus lowering segmentation quality. The major
challenge stems from two key shortcomings: the rarity of high-quality cartoon
dedicated datasets and the absence of competent models for high-resolution
instance extraction on cartoons. To address this, we introduce a high-quality
dataset of over 100k paired high-resolution cartoon images and their instance
labeling masks. We also present an instance-aware image segmentation model that
can generate accurate, high-resolution segmentation masks for characters in
cartoon images. We present that the proposed approach enables a range of
segmentation-dependent cartoon editing applications like 3D Ken Burns parallax
effects, text-guided cartoon style editing, and puppet animation from
illustrations and manga.