WDANet: Exploring Stylized Animation via Diffusion Model for Woodcut‐Style Design

• Published in: Computer animation and virtual worlds Vol. 36; no. 1
• Main Authors: Ou, Yangchunxue, Xu, Jingjun
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.01.2025; Wiley Subscription Services, Inc
• Subjects: Animation; Artists; computer‐aided design; Qualitative analysis; stylized animation; stylized synthesis; Woodcuts; woodcut‐style design
• ISSN: 1546-4261; 1546-427X
• DOI: 10.1002/cav.70007

ABSTRACT Stylized animation strives for innovation and bold visual creativity. Integrating the inherent strong visual impact and color contrast of woodcut style into such animations is both appealing and challenging, especially during the design phase. Traditional woodcut methods, hand‐drawing, and previous computer‐aided techniques face challenges such as dwindling design inspiration, lengthy production times, and complex adjustment procedures. To address these issues, we propose a novel network framework, the Woodcut‐style Design Assistant Network (WDANet). Our research is the first to use diffusion models to streamline the woodcut‐style design process. We curate the Woodcut‐62 dataset, which features works from 62 renowned historical artists, to train WDANet in capturing and learning the aesthetic nuances of woodcut prints. WDANet, based on the denoising U‐Net, effectively decouples content and style features. It allows users to input or slightly modify a text description to quickly generate accurate, high‐quality woodcut‐style designs, saving time and offering flexibility. Quantitative and qualitative analyses, along with user studies, confirm that WDANet outperforms current state‐of‐the‐art methods in generating woodcut‐style images, demonstrating its value as a design aid. We propose a framework called WDANet based on diffusion models to address the challenge of woodcut‐style design in stylized animation. WDANet effectively decouples content and style, improving model fidelity and diversity. We also introduce Woodcut‐62, a dataset for text‐to‐image tasks.