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

TL;DR

The paper introduces IDO-VFI, a novel event-and-frame-based video frame interpolation method that adaptively allocates computational resources using optical flow guidance, achieving high-quality results with reduced computation.

Contribution

It proposes a new adaptive interpolation approach combining optical flow and event data with a Gumbel gating module and Transformer fusion, improving efficiency and accuracy.

Findings

Reduces computation time by 10%

Decreases computational effort by 17%

Outperforms state-of-the-art methods on benchmarks

Abstract

Video frame interpolation aims to generate high-quality intermediate frames from boundary frames and increase frame rate. While existing linear, symmetric and nonlinear models are used to bridge the gap from the lack of inter-frame motion, they cannot reconstruct real motions. Event cameras, however, are ideal for capturing inter-frame dynamics with their extremely high temporal resolution. In this paper, we propose an event-and-frame-based video frame interpolation method named IDO-VFI that assigns varying amounts of computation for different sub-regions via optical flow guidance. The proposed method first estimates the optical flow based on frames and events, and then decides whether to further calculate the residual optical flow in those sub-regions via a Gumbel gating module according to the optical flow amplitude. Intermediate frames are eventually generated through a concise Transformer-based fusion network. Our proposed method maintains high-quality performance while reducing computation time and computational effort by 10% and 17% respectively on Vimeo90K datasets, compared with a unified process on the whole region. Moreover, our method outperforms state-of-the-art frame-only and frames-plus-events methods on multiple video frame interpolation benchmarks. Codes and models are available at https://github.com/shicy17/IDO-VFI.