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We present a slot-wise, object-based transition model that decomposes a sceneinto objects, aligns them (with respect to a slot-wise object memory) tomaintain a consistent order across time, and predicts how those objects evolveover successive frames. The model is trained end-to-end without supervisionusing losses at the level of the object-structured representation rather thanpixels. Thanks to its alignment module, the model deals properly with twoissues that are not handled satisfactorily by other transition models, namelyobject persistence and object identity. We show that the combination of anobject-level loss and correct object alignment over time enables the model tooutperform a state-of-the-art baseline, and allows it to deal well with objectocclusion and re-appearance in partially observable environments.

Unsupervised Object-Based Transition Models for 3D Partially Observable Environments