Neural Point Cloud Diffusion for Disentangled 3D Shape and Appearance
  Generation
Philipp Schröppel Christopher Wewer Jan Eric Lenssen Eddy Ilg Thomas Brox
Controllable generation of 3D assets is important for many practical
applications like content creation in movies, games and engineering, as well as
in AR/VR. Recently, diffusion models have shown remarkable results in
generation quality of 3D objects. However, none of the existing models enable
disentangled generation to control the shape and appearance separately. For the
first time, we present a suitable representation for 3D diffusion models to
enable such disentanglement by introducing a hybrid point cloud and neural
radiance field approach. We model a diffusion process over point positions
jointly with a high-dimensional feature space for a local density and radiance
decoder. While the point positions represent the coarse shape of the object,
the point features allow modeling the geometry and appearance details. This
disentanglement enables us to sample both independently and therefore to
control both separately. Our approach sets a new state of the art in generation
compared to previous disentanglement-capable methods by reduced FID scores of
30-90% and is on-par with other non disentanglement-capable state-of-the art
methods.