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With the ubiquity of rolling shutter (RS) cameras, it is becomingincreasingly attractive to recover the latent global shutter (GS) video fromtwo consecutive RS frames, which also places a higher demand on realism.Existing solutions, using deep neural networks or optimization, achievepromising performance. However, these methods generate intermediate GS framesthrough image warping based on the RS model, which inevitably result in blackholes and noticeable motion artifacts. In this paper, we alleviate these issuesby proposing a context-aware GS video reconstruction architecture. Itfacilitates the advantages such as occlusion reasoning, motion compensation,and temporal abstraction. Specifically, we first estimate the bilateral motionfield so that the pixels of the two RS frames are warped to a common GS frameaccordingly. Then, a refinement scheme is proposed to guide the GS framesynthesis along with bilateral occlusion masks to produce high-fidelity GSvideo frames at arbitrary times. Furthermore, we derive an approximatedbilateral motion field model, which can serve as an alternative to provide asimple but effective GS frame initialization for related tasks. Experiments onsynthetic and real data show that our approach achieves superior performanceover state-of-the-art methods in terms of objective metrics and subjectivevisual quality. Code is available at \url{https://github.com/GitCVfb/CVR}.

Context-Aware Video Reconstruction for Rolling Shutter Cameras