  Moire patterns, appearing as color distortions, severely degrade image and
video qualities when filming a screen with digital cameras. Considering the
increasing demands for capturing videos, we study how to remove such
undesirable moire patterns in videos, namely video demoireing. To this end, we
introduce the first hand-held video demoireing dataset with a dedicated data
collection pipeline to ensure spatial and temporal alignments of captured data.
Further, a baseline video demoireing model with implicit feature space
alignment and selective feature aggregation is developed to leverage
complementary information from nearby frames to improve frame-level video
demoireing. More importantly, we propose a relation-based temporal consistency
loss to encourage the model to learn temporal consistency priors directly from
ground-truth reference videos, which facilitates producing temporally
consistent predictions and effectively maintains frame-level qualities.
Extensive experiments manifest the superiority of our model. Code is available
at \url{https://daipengwa.github.io/VDmoire_ProjectPage/}.
