Capture More, Synthesize Better: Video Frame Interpolation With Larger Receptive Field and Structural Priors

A large receptive field is crucial for the video frame interpolation (VFI) task. Existing video frame interpolation methods struggle with large motions due to their limited receptive fields. However, simply expanding the receptive field brings two challenges: a substantial computational burden and potential loss of texture details. In this paper, we first propose a novel spatial-temporal global window self-attention mechanism with an enlarged receptive field to enhance motion capture. Furthermore, to reduce the computational complexity introduced by the global window, we design a simple and effective separable fence window decomposition. Meanwhile, to better synthesize high-quality intermediate frames, we propose two complementary frame synthesis strategies. First, from the perspective of receptive field design, we introduce a progressive receptive field focusing module, enabling a smooth transition from global motion modeling to local detail preservation. Second, based on the VFI-specific property and the high structural similarity shared by the adjacent frames, we propose a structure-aware synthesis strategy, which incorporates structural priors to guide the generation of fine details. Subjective and objective experimental results demonstrate that our method effectively captures large motions while synthesizing texture details, outperforming state-of-the-art techniques on various datasets.