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Unsupervised landmark learning is the task of learning semantic keypoint-likerepresentations without the use of expensive input keypoint-level annotations.A popular approach is to factorize an image into a pose and appearance datastream, then to reconstruct the image from the factorized components. The poserepresentation should capture a set of consistent and tightly localizedlandmarks in order to facilitate reconstruction of the input image. Ultimately,we wish for our learned landmarks to focus on the foreground object ofinterest. However, the reconstruction task of the entire image forces the modelto allocate landmarks to model the background. This work explores the effectsof factorizing the reconstruction task into separate foreground and backgroundreconstructions, conditioning only the foreground reconstruction on theunsupervised landmarks. Our experiments demonstrate that the proposedfactorization results in landmarks that are focused on the foreground object ofinterest. Furthermore, the rendered background quality is also improved, as thebackground rendering pipeline no longer requires the ill-suited landmarks tomodel its pose and appearance. We demonstrate this improvement in the contextof the video-prediction task.

Unsupervised Disentanglement of Pose, Appearance and Background from Images and Videos