Disentangled 3D Scene Generation with Layout Learning

arXiv:2402.16936v1 Announce Type: cross
Abstract: We introduce a method to generate 3D scenes that are disentangled into their component objects. This disentanglement is unsupervised, relying only on the knowledge of a large pretrained text-to-image model. Our key insight is that objects can be discovered by finding parts of a 3D scene that, when rearranged spatially, still produce valid configurations of the same scene. Concretely, our method jointly optimizes multiple NeRFs from scratch - each representing its own object - along …

3d scene generation arxiv cs.cv cs.lg type