  We present TetGAN, a convolutional neural network designed to generate
tetrahedral meshes. We represent shapes using an irregular tetrahedral grid
which encodes an occupancy and displacement field. Our formulation enables
defining tetrahedral convolution, pooling, and upsampling operations to
synthesize explicit mesh connectivity with variable topological genus. The
proposed neural network layers learn deep features over each tetrahedron and
learn to extract patterns within spatial regions across multiple scales. We
illustrate the capabilities of our technique to encode tetrahedral meshes into
a semantically meaningful latent-space which can be used for shape editing and
synthesis. Our project page is at https://threedle.github.io/tetGAN/.
