  In this paper, we present a new inpainting framework for recovering missing
regions of video frames. Compared with image inpainting, performing this task
on video presents new challenges such as how to preserving temporal consistency
and spatial details, as well as how to handle arbitrary input video size and
length fast and efficiently. Towards this end, we propose a novel deep learning
architecture which incorporates ConvLSTM and optical flow for modeling the
spatial-temporal consistency in videos. It also saves much computational
resource such that our method can handle videos with larger frame size and
arbitrary length streamingly in real-time. Furthermore, to generate an accurate
optical flow from corrupted frames, we propose a robust flow generation module,
where two sources of flows are fed and a flow blending network is trained to
fuse them. We conduct extensive experiments to evaluate our method in various
scenarios and different datasets, both qualitatively and quantitatively. The
experimental results demonstrate the superior of our method compared with the
state-of-the-art inpainting approaches.
