  We present PrimDiffusion, the first diffusion-based framework for 3D human
generation. Devising diffusion models for 3D human generation is difficult due
to the intensive computational cost of 3D representations and the articulated
topology of 3D humans. To tackle these challenges, our key insight is operating
the denoising diffusion process directly on a set of volumetric primitives,
which models the human body as a number of small volumes with radiance and
kinematic information. This volumetric primitives representation marries the
capacity of volumetric representations with the efficiency of primitive-based
rendering. Our PrimDiffusion framework has three appealing properties: 1)
compact and expressive parameter space for the diffusion model, 2) flexible 3D
representation that incorporates human prior, and 3) decoder-free rendering for
efficient novel-view and novel-pose synthesis. Extensive experiments validate
that PrimDiffusion outperforms state-of-the-art methods in 3D human generation.
Notably, compared to GAN-based methods, our PrimDiffusion supports real-time
rendering of high-quality 3D humans at a resolution of $512\times512$ once the
denoising process is done. We also demonstrate the flexibility of our framework
on training-free conditional generation such as texture transfer and 3D
inpainting.
