  Videos contain highly redundant information between frames. Such redundancy
has been extensively studied in video compression and encoding, but is less
explored for more advanced video processing. In this paper, we propose a
learnable unified framework for propagating a variety of visual properties of
video images, including but not limited to color, high dynamic range (HDR), and
segmentation information, where the properties are available for only a few
key-frames. Our approach is based on a temporal propagation network (TPN),
which models the transition-related affinity between a pair of frames in a
purely data-driven manner. We theoretically prove two essential factors for
TPN: (a) by regularizing the global transformation matrix as orthogonal, the
"style energy" of the property can be well preserved during propagation; (b)
such regularization can be achieved by the proposed switchable TPN with
bi-directional training on pairs of frames. We apply the switchable TPN to
three tasks: colorizing a gray-scale video based on a few color key-frames,
generating an HDR video from a low dynamic range (LDR) video and a few HDR
frames, and propagating a segmentation mask from the first frame in videos.
Experimental results show that our approach is significantly more accurate and
efficient than the state-of-the-art methods.
