Recent video frame interpolation (VFI) methods rely on computationally heavy modules (e.g., global attention module) to handle large motions, incurring prohibitive costs which hinders their practical real-time deployment. In this work, we revisit the core objective of VFI: enhancing the temporal resolution of videos. We identify that previous VFI’s frame-isolated processing ignores continuous temporal modeling, introduces computational redundancy in video streaming scenarios. To address this, we propose an online learning recurrent net with inertia-aware motion estimation(IMEVSI). It consists of implicit motion propagation( IMP), explicit motion propagation(EMP) and adaptive online learning strategy(AOL). IMP and EMP are used to high order inter-frame motion modeling considering motion inertia, AOL are proposed to bridge the motion domain gap between training and deployment. For IMP, we initiate from explicit physical motion modeling, progressively integrating learnable parameters into inertia-ware motion extraction and finally unify motion propagation and extraction within our recurrent motion propagation Transformer(RMPT). For EMP, we directly inject adjacent motion into current flow estimation recognizing its inertia contribution. For AOL,we leverage cycle consistency to dynamically adjust intermediate flow estimator and maintains an adaptive threshold to control parameter update. Extensive experiments demonstrate that our method outperforms state-of-the-art (SOTA) approaches on regular, large-motion, and high-resolution benchmarks while achieving excellent inference speed and FLOPs.

Intermediate flow estimation is an important part of video frame interpolation (VFI). Most previous works use interpolation to derive the intermediate flow assuming localized linear motion. However, this method is not effective when dealing with extreme motions. In this work, we assume that the motion trajectory of an object is determined by the appearance characteristics of this object. Based on this assumption, we propose a new intermediate flow estimation method, which obtains the motion features of intermediate frames from image appearance and inter-frame motion features. In addition, in order to fully extract the inter-frame features, we rethink the difference of VFI and previous works on using Swin-Transformer and compute the appearance features and motion features within the adaptive neighborhood by cyclically shifting the window. Experimental results show that our method achieves state-of-the-art performance on different datasets for both fixed-time and arbitrary-time interpolation. Moreover, our proposed method outperforms models that require inputting a sequence of four frames when handling videos with extremely large motion. The source code is available from https://github.com/chen12304/IFE-VFI.