Frankenstein: Generating Semantic-Compositional 3D Scenes in One
  Tri-Plane
Han Yan Yang Li Zhennan Wu Shenzhou Chen Weixuan Sun Taizhang Shang Weizhe Liu Tian Chen Xiaqiang Dai Chao Ma Hongdong Li Pan Ji
We present Frankenstein, a diffusion-based framework that can generate
semantic-compositional 3D scenes in a single pass. Unlike existing methods that
output a single, unified 3D shape, Frankenstein simultaneously generates
multiple separated shapes, each corresponding to a semantically meaningful
part. The 3D scene information is encoded in one single tri-plane tensor, from
which multiple Singed Distance Function (SDF) fields can be decoded to
represent the compositional shapes. During training, an auto-encoder compresses
tri-planes into a latent space, and then the denoising diffusion process is
employed to approximate the distribution of the compositional scenes.
Frankenstein demonstrates promising results in generating room interiors as
well as human avatars with automatically separated parts. The generated scenes
facilitate many downstream applications, such as part-wise re-texturing, object
rearrangement in the room or avatar cloth re-targeting.