  With the ubiquity of rolling shutter (RS) cameras, it is becoming
increasingly attractive to recover the latent global shutter (GS) video from
two consecutive RS frames, which also places a higher demand on realism.
Existing solutions, using deep neural networks or optimization, achieve
promising performance. However, these methods generate intermediate GS frames
through image warping based on the RS model, which inevitably result in black
holes and noticeable motion artifacts. In this paper, we alleviate these issues
by proposing a context-aware GS video reconstruction architecture. It
facilitates the advantages such as occlusion reasoning, motion compensation,
and temporal abstraction. Specifically, we first estimate the bilateral motion
field so that the pixels of the two RS frames are warped to a common GS frame
accordingly. Then, a refinement scheme is proposed to guide the GS frame
synthesis along with bilateral occlusion masks to produce high-fidelity GS
video frames at arbitrary times. Furthermore, we derive an approximated
bilateral motion field model, which can serve as an alternative to provide a
simple but effective GS frame initialization for related tasks. Experiments on
synthetic and real data show that our approach achieves superior performance
over state-of-the-art methods in terms of objective metrics and subjective
visual quality. Code is available at \url{https://github.com/GitCVfb/CVR}.
