CPNet: Cartoon Parsing with Pixel and Part Correlation

Cartoon parsing, the task of segmenting constituent parts such as heads, arms, and legs of cartoon characters, holds substantial significance for applications in the animation industry and emerging metaverse. Nonetheless, this domain presents considerable challenges stemming from complex visual appearances, irregular structures, abstract drawing styles, among other factors. In this paper, a novel Cartoon Parsing Network (CPNet) is introduced to address these challenges. CPNet skillfully leverages the spatial and semantic correlations of pixels to discern intricate and visually akin appearances. Furthermore, it employs both local and global correlations of constituent parts to differentiate irregular and abstract body sections. Specifically, the pixels of the cartoon image are interconnected by capitalizing on the spatial and semantic correlations. To this end, a center point predictor, working in tandem with a pixel-aware attention, facilitates the exploration of pixel-level correlation learning. Additionally, the various constituent parts are meticulously organized to resonate with the intrinsic physiological structure of a cartoon character. The character's graph structure is assembled and analyzed by an edge-aware graph neural network, thereby linking adjacent parts and assimilating local correlations. A part-guided non-local attention mechanism is fashioned to correlate individual parts with the entire body, thereby modeling global connections. In addition, a new dataset named CartoonSet is curated and annotated explicitly for cartoon parsing. Experiments carried out on both cartoon parsing and human parsing datasets yield compelling results, thereby attesting to the efficacy and innovativeness of the proposed method.