Analyzing human motion is a challenging task with a wide variety of
applications in computer vision and in graphics. One such application, of
particular importance in computer animation, is the retargeting of motion from
one performer to another. While humans move in three dimensions, the vast
majority of human motions are captured using video, requiring 2D-to-3D pose and
camera recovery, before existing retargeting approaches may be applied. In this
paper, we present a new method for retargeting video-captured motion between
different human performers, without the need to explicitly reconstruct 3D poses
and/or camera parameters. In order to achieve our goal, we learn to extract,
directly from a video, a high-level latent motion representation, which is
invariant to the skeleton geometry and the camera view. Our key idea is to
train a deep neural network to decompose temporal sequences of 2D poses into
three components: motion, skeleton, and camera view-angle. Having extracted
such a representation, we are able to re-combine motion with novel skeletons
and camera views, and decode a retargeted temporal sequence, which we compare
to a ground truth from a synthetic dataset. We demonstrate that our framework
can be used to robustly extract human motion from videos, bypassing 3D
reconstruction, and outperforming existing retargeting methods, when applied to
videos in-the-wild. It also enables additional applications, such as
performance cloning, video-driven cartoons, and motion retrieval.

本文提出了一种新的方法，通过使用神经网络从视频中提取人体运动的高级潜在表示的方法，实现了不需要显式重建三维姿态和 / 或相机参数的不同人类表演者之间的视频捕获运动的再定向。