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The 2D animation workflow is typically initiated with the creation ofkeyframes using sketch-based drawing. Subsequent inbetweens (i.e., intermediatesketch frames) are crafted through manual interpolation for smooth animations,which is a labor-intensive process. Thus, the prospect of automatic animationsketch interpolation has become highly appealing. However, existing videointerpolation methods are generally hindered by two key issues for sketchinbetweening: 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, namely Fine-to-Coarse SketchInterpolation Network (FC-SIN). This approach incorporates multi-level guidancethat formulates region-level correspondence, sketch-level correspondence andpixel-level dynamics. A multi-stream U-Transformer is then devised tocharacterize sketch inbewteening patterns using these multi-level guidesthrough the integration of both self-attention and cross-attention mechanisms.Additionally, to facilitate future research on animation sketch inbetweening,we constructed a large-scale dataset - STD-12K, comprising 30 sketch animationseries in diverse artistic styles. Comprehensive experiments on this datasetconvincingly show that our proposed FC-SIN surpasses the state-of-the-artinterpolation methods. Our code and dataset will be publicly available.

Bridging the Gap: Fine-to-Coarse Sketch Interpolation Network for High-Quality Animation Sketch Inbetweening