  Commercially available light field cameras have difficulty in capturing 5D
(4D + time) light field videos. They can only capture still light filed images
or are excessively expensive for normal users to capture the light field video.
To tackle this problem, we propose a deep learning-based method for
synthesizing a light field video from a monocular video. We propose a new
synthetic light field video dataset that renders photorealistic scenes using
UnrealCV rendering engine because no light field dataset is available. The
proposed deep learning framework synthesizes the light field video with a full
set (9$\times$9) of sub-aperture images from a normal monocular video. The
proposed network consists of three sub-networks, namely, feature extraction, 5D
light field video synthesis, and temporal consistency refinement. Experimental
results show that our model can successfully synthesize the light field video
for synthetic and actual scenes and outperforms the previous frame-by-frame
methods quantitatively and qualitatively. The synthesized light field can be
used for conventional light field applications, namely, depth estimation,
viewpoint change, and refocusing.
