  Recent work has shown that object-centric representations can greatly help
improve the accuracy of learning dynamics while also bringing interpretability.
In this work, we take this idea one step further, ask the following question:
"can learning disentangled representation further improve the accuracy of
visual dynamics prediction in object-centric models?" While there has been some
attempt to learn such disentangled representations for the case of static
images \citep{nsb}, to the best of our knowledge, ours is the first work which
tries to do this in a general setting for video, without making any specific
assumptions about the kind of attributes that an object might have. The key
building block of our architecture is the notion of a {\em block}, where
several blocks together constitute an object. Each block is represented as a
linear combination of a given number of learnable concept vectors, which is
iteratively refined during the learning process. The blocks in our model are
discovered in an unsupervised manner, by attending over object masks, in a
style similar to discovery of slots \citep{slot_attention}, for learning a
dense object-centric representation. We employ self-attention via transformers
over the discovered blocks to predict the next state resulting in discovery of
visual dynamics. We perform a series of experiments on several benchmark 2-D,
and 3-D datasets demonstrating that our architecture (1) can discover
semantically meaningful blocks (2) help improve accuracy of dynamics prediction
compared to SOTA object-centric models (3) perform significantly better in OOD
setting where the specific attribute combinations are not seen earlier during
training. Our experiments highlight the importance discovery of disentangled
representation for visual dynamics prediction.
