TensorX
返回文献探索

Paper · arXiv 2412.14173

AniDoc: Animation Creation Made Easier

Yihao Meng, Hao Ouyang, Hanlin Wang, Qiuyu Wang, Wen Wang, Ka Leong Cheng, Zhiheng Liu, Yujun Shen, Huamin Qu

58 upvotesDecember 18, 2024arXiv 预印本
AI 摘要

AniDoc uses video diffusion models to automate colorization and in-betweening in 2D animation, improving efficiency by leveraging correspondence matching.

video diffusion modelscorrespondence matchinganidockeyframe animationin-betweeningcolorizationline arttemporal consistency

Abstract

The production of 2D animation follows an industry-standard workflow, encompassing four essential stages: character design, keyframe animation, in-betweening, and coloring. Our research focuses on reducing the labor costs in the above process by harnessing the potential of increasingly powerful generative AI. Using video diffusion models as the foundation, AniDoc emerges as a video line art colorization tool, which automatically converts sketch sequences into colored animations following the reference character specification. Our model exploits correspondence matching as an explicit guidance, yielding strong robustness to the variations (e.g., posture) between the reference character and each line art frame. In addition, our model could even automate the in-betweening process, such that users can easily create a temporally consistent animation by simply providing a character image as well as the start and end sketches. Our code is available at: https://yihao-meng.github.io/AniDoc_demo.

北京市昌平区探索星信息技术及软件开发工作室

京ICP备2026059466号
AniDoc: Animation Creation Made Easier | TensorX