  Image alignment by mesh warps, such as meshflow, is a fundamental task which
has been widely applied in various vision applications(e.g., multi-frame
HDR/denoising, video stabilization). Traditional mesh warp methods detect and
match image features, where the quality of alignment highly depends on the
quality of image features. However, the image features are not robust in
occurrence of low-texture and low-light scenes. Deep homography methods, on the
other hand, are free from such problem by learning deep features for robust
performance. However, a homography is limited to plane motions. In this work,
we present a deep meshflow motion model, which takes two images as input and
output a sparse motion field with motions located at mesh vertexes. The deep
meshflow enjoys the merics of meshflow that can describe nonlinear motions
while also shares advantage of deep homography that is robust against
challenging textureless scenarios. In particular, a new unsupervised network
structure is presented with content-adaptive capability. On one hand, the image
content that cannot be aligned under mesh representation are rejected by our
learned mask, similar to the RANSAC procedure. On the other hand, we learn
multiple mesh resolutions, combining to a non-uniform mesh division. Moreover,
a comprehensive dataset is presented, covering various scenes for training and
testing. The comparison between both traditional mesh warp methods and deep
based methods show the effectiveness of our deep meshflow motion model.
