  When perceiving the world from multiple viewpoints, humans have the ability
to reason about the complete objects in a compositional manner even when an
object is completely occluded from certain viewpoints. Meanwhile, humans are
able to imagine novel views after observing multiple viewpoints. Recent
remarkable advances in multi-view object-centric learning still leaves some
unresolved problems: 1) The shapes of partially or completely occluded objects
can not be well reconstructed. 2) The novel viewpoint prediction depends on
expensive viewpoint annotations rather than implicit rules in view
representations. In this paper, we introduce a time-conditioned generative
model for videos. To reconstruct the complete shape of an object accurately, we
enhance the disentanglement between the latent representations of objects and
views, where the latent representations of time-conditioned views are jointly
inferred with a Transformer and then are input to a sequential extension of
Slot Attention to learn object-centric representations. In addition, Gaussian
processes are employed as priors of view latent variables for video generation
and novel-view prediction without viewpoint annotations. Experiments on
multiple datasets demonstrate that the proposed model can make object-centric
video decomposition, reconstruct the complete shapes of occluded objects, and
make novel-view predictions.
