  We introduce ToonCrafter, a novel approach that transcends traditional
correspondence-based cartoon video interpolation, paving the way for generative
interpolation. Traditional methods, that implicitly assume linear motion and
the absence of complicated phenomena like dis-occlusion, often struggle with
the exaggerated non-linear and large motions with occlusion commonly found in
cartoons, resulting in implausible or even failed interpolation results. To
overcome these limitations, we explore the potential of adapting live-action
video priors to better suit cartoon interpolation within a generative
framework. ToonCrafter effectively addresses the challenges faced when applying
live-action video motion priors to generative cartoon interpolation. First, we
design a toon rectification learning strategy that seamlessly adapts
live-action video priors to the cartoon domain, resolving the domain gap and
content leakage issues. Next, we introduce a dual-reference-based 3D decoder to
compensate for lost details due to the highly compressed latent prior spaces,
ensuring the preservation of fine details in interpolation results. Finally, we
design a flexible sketch encoder that empowers users with interactive control
over the interpolation results. Experimental results demonstrate that our
proposed method not only produces visually convincing and more natural
dynamics, but also effectively handles dis-occlusion. The comparative
evaluation demonstrates the notable superiority of our approach over existing
competitors.
