We propose KeypointGAN, a new method for recognizing the pose of objects from
a single image that for learning uses only unlabelled videos and a weak
empirical prior on the object poses. Video frames differ primarily in the pose
of the objects they contain, so our method distils the pose information by
analyzing the differences between frames. The distillation uses a new dual
representation of the geometry of objects as a set of 2D keypoints, and as a
pictorial representation, i.e. a skeleton image. This has three benefits: (1)
it provides a tight `geometric bottleneck' which disentangles pose from
appearance, (2) it can leverage powerful image-to-image translation networks to
map between photometry and geometry, and (3) it allows to incorporate empirical
pose priors in the learning process. The pose priors are obtained from unpaired
data, such as from a different dataset or modality such as mocap, such that no
annotated image is ever used in learning the pose recognition network. In
standard benchmarks for pose recognition for humans and faces, our method
achieves state-of-the-art performance among methods that do not require any
labelled images for training.

本文提出了 KeypointGAN 方法，通过从未标注的视频和基于弱领域先验知识的学习，仅使用单个图像就可以识别物体的姿态，利用一个新的物体的双重表示方法，并且这种方法可以在不使用标注图像的情况下获得最新的成果。

无监督学习视频中的可解释关键点

Self-supervised Learning of Interpretable Keypoints from Unlabelled  Videos

Gaussian Graphical Models provide a convenient framework for representing
dependencies between variables. Recently, this tool has received a high
interest for the discovery of biological networks. The literature focuses on
the case where a single network is inferred from a set of measurements, but, as
wetlab data is typically scarce, several assays, where the experimental
conditions affect interactions, are usually merged to infer a single network.
In this paper, we propose two approaches for estimating multiple related
graphs, by rendering the closeness assumption into an empirical prior or group
penalties. We provide quantitative results demonstrating the benefits of the
proposed approaches. The methods presented in this paper are embeded in the R
package 'simone' from version 1.0-0 and later.

本文提出了两种估计多个相关图的方法，并将亲密度假设转化为经验先验或组惩罚。我们提供了定量结果，证明了所提出方法的优势。该论文介绍的方法已嵌入 R 软件包'simone' 版本 1.0-0 及更高版本中。