  We present Sequential Attend, Infer, Repeat (SQAIR), an interpretable deep
generative model for videos of moving objects. It can reliably discover and
track objects throughout the sequence of frames, and can also generate future
frames conditioning on the current frame, thereby simulating expected motion of
objects. This is achieved by explicitly encoding object presence, locations and
appearances in the latent variables of the model. SQAIR retains all strengths
of its predecessor, Attend, Infer, Repeat (AIR, Eslami et. al., 2016),
including learning in an unsupervised manner, and addresses its shortcomings.
We use a moving multi-MNIST dataset to show limitations of AIR in detecting
overlapping or partially occluded objects, and show how SQAIR overcomes them by
leveraging temporal consistency of objects. Finally, we also apply SQAIR to
real-world pedestrian CCTV data, where it learns to reliably detect, track and
generate walking pedestrians with no supervision.
