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.

Frankenstein是一个基于扩散的框架，可以在单次传递中生成语义组合的三维场景。与现有方法不同，Frankenstein同时生成多个分离的形状，每个形状对应于一个语义上有意义的部分。生成的场景可以用三元面张量编码，从中可以解码出多个有组合性的物体形状。Frankenstein在生成房间内部以及带有自动分离部分的人体角色方面展示了良好的结果。生成的场景可以用于许多下游应用，如部分纹理重建、房间内物体重新摆放或角色服装重新定位。

Frankenstein：在一台三平面机器上生成语义组合的3D场景