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Video frame interpolation aims to generate high-quality intermediate framesfrom boundary frames and increase frame rate. While existing linear, symmetricand nonlinear models are used to bridge the gap from the lack of inter-framemotion, they cannot reconstruct real motions. Event cameras, however, are idealfor capturing inter-frame dynamics with their extremely high temporalresolution. In this paper, we propose an event-and-frame-based video frameinterpolation method named IDO-VFI that assigns varying amounts of computationfor different sub-regions via optical flow guidance. The proposed method firstestimates the optical flow based on frames and events, and then decides whetherto further calculate the residual optical flow in those sub-regions via aGumbel gating module according to the optical flow amplitude. Intermediateframes are eventually generated through a concise Transformer-based fusionnetwork. Our proposed method maintains high-quality performance while reducingcomputation time and computational effort by 10% and 17% respectively onVimeo90K datasets, compared with a unified process on the whole region.Moreover, our method outperforms state-of-the-art frame-only andframes-plus-events methods on multiple video frame interpolation benchmarks.Codes and models are available at https://github.com/shicy17/IDO-VFI.

IDO-VFI: Identifying Dynamics via Optical Flow Guidance for Video Frame Interpolation with Events