IMEVSI: Online Adaptive Video Stream Interpolation via Inertia-Aware Motion Estimation

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.