  Auto-Regressive (AR) models have achieved impressive results in 2D image
generation by modeling joint distributions in the grid space. While this
approach has been extended to the 3D domain for powerful shape generation, it
still has two limitations: expensive computations on volumetric grids and
ambiguous auto-regressive order along grid dimensions. To overcome these
limitations, we propose the Improved Auto-regressive Model (ImAM) for 3D shape
generation, which applies discrete representation learning based on a latent
vector instead of volumetric grids. Our approach not only reduces computational
costs but also preserves essential geometric details by learning the joint
distribution in a more tractable order. Moreover, thanks to the simplicity of
our model architecture, we can naturally extend it from unconditional to
conditional generation by concatenating various conditioning inputs, such as
point clouds, categories, images, and texts. Extensive experiments demonstrate
that ImAM can synthesize diverse and faithful shapes of multiple categories,
achieving state-of-the-art performance.
