Dual-Guided Video Frame Interpolation With Spatial-Temporal Global Attention

Video frame interpolation technology improves visual experience with the development of deep learning. However, capturing large motions while synthesizing fine texture details remains a challenging task. Regarding large motion scenarios, some pioneering Transformer-based methods primarily rely on local attention, which does not fully leverage the global receptive field advantage. To address this issue, this paper proposes to further broaden the receptive field of the Transformer to capture more correlations in the video frame interpolation task. Specifically, we propose a global self-attention mechanism in the form of spatial-temporal separation. Regarding texture details, since roughly enlarging the receptive field results in the loss of details, we propose to use large motion information in both feature and pixel spaces as a dual-guided prior to enhance detail synthesis. The separable attention mechanism and the straightforward frame synthesis design significantly enhance the resource efficiency of our model. Extensive experiments show that our method achieves state-of-the-art performance, effectively capturing large motions and preserving texture details.