We propose an attention-based networks for transferring motions between
arbitrary objects. Given a source image(s) and a driving video, our networks
animate the subject in the source images according to the motion in the driving
video. In our attention mechanism, dense similarities between the learned
keypoints in the source and the driving images are computed in order to
retrieve the appearance information from the source images. Taking a different
approach from the well-studied warping based models, our attention-based model
has several advantages. By reassembling non-locally searched pieces from the
source contents, our approach can produce more realistic outputs. Furthermore,
our system can make use of multiple observations of the source appearance (e.g.
front and sides of faces) to make the results more accurate. To reduce the
training-testing discrepancy of the self-supervised learning, a novel
cross-identity training scheme is additionally introduced. With the training
scheme, our networks is trained to transfer motions between different subjects,
as in the real testing scenario. Experimental results validate that our method
produces visually pleasing results in various object domains, showing better
performances compared to previous works.

本文提出了一种基于注意力机制的网络来进行任意物体之间的动作传递，从源图像和驱动视频中提取外观信息并实现不同主体间的动作传递，通过交叉主体训练模型能更多角度、更真实的传递动作，实验表明该方法在各个领域内表现更好。