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In this paper, we consider the task of space-time video super-resolution(ST-VSR), namely, expanding a given source video to a higher frame rate andresolution simultaneously. However, most existing schemes either consider afixed intermediate time and scale in the training stage or only accept a presetnumber of input frames (e.g., two adjacent frames) that fails to exploitlong-range temporal information. To address these problems, we propose acontinuous ST-VSR (C-STVSR) method that can convert the given video to anyframe rate and spatial resolution. To achieve time-arbitrary interpolation, wepropose a forward warping guided frame synthesis module and anoptical-flow-guided context consistency loss to better approximate extrememotion and preserve similar structures among input and prediction frames. Inaddition, we design a memory-friendly cascading depth-to-space module torealize continuous spatial upsampling. Meanwhile, with the sophisticatedreorganization of optical flow, the proposed method is memory friendly, makingit possible to propagate information from long-range neighboring frames andachieve better reconstruction quality. Extensive experiments show that theproposed algorithm has good flexibility and achieves better performance onvarious datasets compared with the state-of-the-art methods in both objectiveevaluations and subjective visual effects.

Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information