  We tackle the problem of estimating correspondences from a general marker,
such as a movie poster, to an image that captures such a marker.
Conventionally, this problem is addressed by fitting a homography model based
on sparse feature matching. However, they are only able to handle plane-like
markers and the sparse features do not sufficiently utilize appearance
information. In this paper, we propose a novel framework NeuralMarker, training
a neural network estimating dense marker correspondences under various
challenging conditions, such as marker deformation, harsh lighting, etc.
Besides, we also propose a novel marker correspondence evaluation method
circumstancing annotations on real marker-image pairs and create a new
benchmark. We show that NeuralMarker significantly outperforms previous methods
and enables new interesting applications, including Augmented Reality (AR) and
video editing.
