  Unsupervised learning of object-centric representations in dynamic visual
scenes is challenging. Unlike most previous approaches that learn to decompose
2D images, we present DynaVol, a 3D scene generative model that unifies
geometric structures and object-centric learning in a differentiable volume
rendering framework. The key idea is to perform object-centric voxelization to
capture the 3D nature of the scene, which infers the probability distribution
over objects at individual spatial locations. These voxel features evolve over
time through a canonical-space deformation function, forming the basis for
global representation learning via slot attention. The voxel features and
global features are complementary and are both leveraged by a compositional
NeRF decoder for volume rendering. DynaVol remarkably outperforms existing
approaches for unsupervised dynamic scene decomposition. Once trained, the
explicitly meaningful voxel features enable additional capabilities that 2D
scene decomposition methods cannot achieve: it is possible to freely edit the
geometric shapes or manipulate the motion trajectories of the objects.
