  Significant progress has been made in training large generative models for
natural language and images. Yet, the advancement of 3D generative models is
hindered by their substantial resource demands for training, along with
inefficient, non-compact, and less expressive representations. This paper
introduces Make-A-Shape, a new 3D generative model designed for efficient
training on a vast scale, capable of utilizing 10 millions publicly-available
shapes. Technical-wise, we first innovate a wavelet-tree representation to
compactly encode shapes by formulating the subband coefficient filtering scheme
to efficiently exploit coefficient relations. We then make the representation
generatable by a diffusion model by devising the subband coefficients packing
scheme to layout the representation in a low-resolution grid. Further, we
derive the subband adaptive training strategy to train our model to effectively
learn to generate coarse and detail wavelet coefficients. Last, we extend our
framework to be controlled by additional input conditions to enable it to
generate shapes from assorted modalities, e.g., single/multi-view images, point
clouds, and low-resolution voxels. In our extensive set of experiments, we
demonstrate various applications, such as unconditional generation, shape
completion, and conditional generation on a wide range of modalities. Our
approach not only surpasses the state of the art in delivering high-quality
results but also efficiently generates shapes within a few seconds, often
achieving this in just 2 seconds for most conditions.
