  We propose replacing scene text in videos using deep style transfer and
learned photometric transformations.Building on recent progress on still image
text replacement,we present extensions that alter text while preserving the
appearance and motion characteristics of the original video.Compared to the
problem of still image text replacement,our method addresses additional
challenges introduced by video, namely effects induced by changing lighting,
motion blur, diverse variations in camera-object pose over time,and
preservation of temporal consistency. We parse the problem into three steps.
First, the text in all frames is normalized to a frontal pose using a
spatio-temporal trans-former network. Second, the text is replaced in a single
reference frame using a state-of-art still-image text replacement method.
Finally, the new text is transferred from the reference to remaining frames
using a novel learned image transformation network that captures lighting and
blur effects in a temporally consistent manner. Results on synthetic and
challenging real videos show realistic text trans-fer, competitive quantitative
and qualitative performance,and superior inference speed relative to
alternatives. We introduce new synthetic and real-world datasets with paired
text objects. To the best of our knowledge this is the first attempt at deep
video text replacement.
