Disentangled 3D Scene Generation with Layout Learning

• URL: http://arxiv.org/abs/2402.16936v1
• Date: Mon, 26 Feb 2024 18:54:15 GMT
• Title: Disentangled 3D Scene Generation with Layout Learning
• Authors: Dave Epstein, Ben Poole, Ben Mildenhall, Alexei A. Efros, Aleksander Holynski
• Abstract summary: We introduce a method to generate 3D scenes that are disentangled into their component objects. 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. We show that despite its simplicity, our approach successfully generates 3D scenes into individual objects.
• Score: 109.03233745767062
• License: http://creativecommons.org/licenses/by/4.0/
• 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 with a set of layouts that composite these objects into scenes. We then encourage these composited scenes to be in-distribution according to the image generator. We show that despite its simplicity, our approach successfully generates 3D scenes decomposed into individual objects, enabling new capabilities in text-to-3D content creation. For results and an interactive demo, see our project page at https://dave.ml/layoutlearning/

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