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, AniDoc1 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://yihaomeng.github.io/AniDocdemo.
| Original language | English |
|---|---|
| Pages (from-to) | 18187-18197 |
| Number of pages | 11 |
| Journal | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
| Early online date | 13 Aug 2025 |
| DOIs | |
| Publication status | Published - 2025 |
| Event | 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2025 - Nashville, United States Duration: 10 Jun 2025 → 17 Jun 2025 |
Bibliographical note
Publisher Copyright:© 2025 IEEE.
Keywords
- animation
- diffusion models
- line art video colorization
- video generation
- video interpolation
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