CVPR 2025poster6 citations

SceneFactor: Factored Latent 3D Diffusion for Controllable 3D Scene Generation

Aleksey Bokhovkin, Quan Meng, Shubham Tulsiani, Angela Dai

Abstract

We present SceneFactor, a diffusion-based approach for large-scale 3D scene generation that enables controllable generation and effortless editing. SceneFactor enables text-guided 3D scene synthesis through our factored diffusion formulation, leveraging latent semantic and geometric manifolds for generation of arbitrary-sized 3D scenes. While text input enables easy, controllable generation, text guidance remains imprecise for intuitive, localized editing and manipulation of the generated 3D scenes. Our factored semantic diffusion generates a proxy semantic space composed of semantic 3D boxes that enables controllable editing of generated scenes by adding, removing, changing the size of the semantic 3D proxy boxes that guides high-fidelity, consistent 3D geometric editing. Extensive experiments demonstrate that our approach enables high-fidelity 3D scene synthesis with effective controllable editing through our factored diffusion approach.

BibTeX
@InProceedings{Bokhovkin_2025_CVPR,
    author    = {Bokhovkin, Aleksey and Meng, Quan and Tulsiani, Shubham and Dai, Angela},
    title     = {SceneFactor: Factored Latent 3D Diffusion for Controllable 3D Scene Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {628-639}
}
SceneFactor: Factored Latent 3D Diffusion for Controllable 3D Scene Generation · CVPR 2025