Variational 3D Mesh Generation of Man-Made Objects

Data-driven 3D shape analysis, reconstruction, and
generation is an active research topic that finds many useful
applications in the fields of computer games, computer graphics,
and augmented/virtual reality. Many of the previous mesh-based
generative approaches work on natural shapes such as human faces
and bodies and little work targets man-made objects. This work
proposes a generative probabilistic framework for 3D man-made
mesh shapes. Specifically, it proposes a Variational Autoencoder
that works directly on mesh vertices and encodes meshes into a
probabilistic, smooth, and traversable latent space that can be
sampled after training and decoded to generate novel and plausible
shapes. Extensive experiments show the representational power of
the proposed framework and the underlying latent space.
Operations such as random sample generation, linear
interpolation, and shape arithmetic can be performed using the
proposed method and produce plausible results. An additional
advantage of the proposed framework is that it learns to produce a
disentangled shape representation which gives finer control over
the generated mesh and allows generating shapes with specific
qualities without losing the reconstruction power of the
autoencoder.