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A video autoencoder is proposed for learning disentan- gled representationsof 3D structure and camera pose from videos in a self-supervised manner.Relying on temporal continuity in videos, our work assumes that the 3D scenestructure in nearby video frames remains static. Given a sequence of videoframes as input, the video autoencoder extracts a disentangled representationof the scene includ- ing: (i) a temporally-consistent deep voxel feature torepresent the 3D structure and (ii) a 3D trajectory of camera pose for eachframe. These two representations will then be re-entangled for rendering theinput video frames. This video autoencoder can be trained directly using apixel reconstruction loss, without any ground truth 3D or camera poseannotations. The disentangled representation can be applied to a range oftasks, including novel view synthesis, camera pose estimation, and videogeneration by motion following. We evaluate our method on several large- scalenatural video datasets, and show generalization results on out-of-domainimages.

Video Autoencoder: self-supervised disentanglement of static 3D structure and motion