@inproceedings{
shen2024bridging,
title={Bridging the Gap: Sketch-Aware Interpolation Network for High-Quality Animation Sketch Inbetweening},
author={Jiaming Shen and Kun Hu and Wei Bao and Chang Wen Chen and Zhiyong Wang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=7GPsuT0vyh}
}
Hand-drawn 2D animation workflow is typically initiated with the creation of sketch keyframes. Subsequent manual inbetweens are crafted for smoothness, which is a labor-intensive process and the prospect of automatic animation sketch interpolation has become highly appealing. Yet, common frame interpolation methods are generally hindered by two key issues: 1) limited texture and colour details in sketches, and 2) exaggerated alterations between two sketch keyframes. To overcome these issues, we propose a novel deep learning method - Sketch-Aware Interpolation Network (SAIN). This approach incorporates multi-level guidance that formulates region-level correspondence, stroke-level correspondence and pixel-level dynamics. A multi-stream U-Transformer is then devised to characterize sketch inbewteening patterns using these multi-level guides through the integration of self / cross-attention mechanisms. Additionally, to facilitate future research on animation sketch inbetweening, we constructed a large-scale dataset - STD-12K, comprising 30 sketch animation series in diverse artistic styles. Comprehensive experiments on this dataset convincingly show that our proposed SAIN surpasses the state-of-the-art interpolation methods. Our code and dataset will be publicly available.
Bridging the Gap: Sketch-Aware Interpolation Network for High-Quality Animation Sketch Inbetweening
Jiaming Shen Kun Hu Wei Bao Chang Wen Chen Zhiyong Wang