  We present XCube (abbreviated as $\mathcal{X}^3$), a novel generative model
for high-resolution sparse 3D voxel grids with arbitrary attributes. Our model
can generate millions of voxels with a finest effective resolution of up to
$1024^3$ in a feed-forward fashion without time-consuming test-time
optimization. To achieve this, we employ a hierarchical voxel latent diffusion
model which generates progressively higher resolution grids in a coarse-to-fine
manner using a custom framework built on the highly efficient VDB data
structure. Apart from generating high-resolution objects, we demonstrate the
effectiveness of XCube on large outdoor scenes at scales of 100m$\times$100m
with a voxel size as small as 10cm. We observe clear qualitative and
quantitative improvements over past approaches. In addition to unconditional
generation, we show that our model can be used to solve a variety of tasks such
as user-guided editing, scene completion from a single scan, and text-to-3D.
The source code and more results can be found at
https://research.nvidia.com/labs/toronto-ai/xcube/.
