Lay-A-Scene: Personalized 3D Object Arrangement Using Text-to-Image Priors

• URL: http://arxiv.org/abs/2406.00687v2
• Date: Tue, 4 Jun 2024 16:19:47 GMT
• Title: Lay-A-Scene: Personalized 3D Object Arrangement Using Text-to-Image Priors
• Authors: Ohad Rahamim, Hilit Segev, Idan Achituve, Yuval Atzmon, Yoni Kasten, Gal Chechik,
• Abstract summary: Current 3D generation techniques struggle with generating scenes with multiple high-resolution objects. Here we introduce Lay-A-Scene, which solves the task of Open-set 3D Object Arrangement. We show how to infer the 3D poses and arrangement of objects from a 2D generated image by finding a consistent projection of objects onto the 2D scene.
• Score: 43.19801974707858
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generating 3D visual scenes is at the forefront of visual generative AI, but current 3D generation techniques struggle with generating scenes with multiple high-resolution objects. Here we introduce Lay-A-Scene, which solves the task of Open-set 3D Object Arrangement, effectively arranging unseen objects. Given a set of 3D objects, the task is to find a plausible arrangement of these objects in a scene. We address this task by leveraging pre-trained text-to-image models. We personalize the model and explain how to generate images of a scene that contains multiple predefined objects without neglecting any of them. Then, we describe how to infer the 3D poses and arrangement of objects from a 2D generated image by finding a consistent projection of objects onto the 2D scene. We evaluate the quality of Lay-A-Scene using 3D objects from Objaverse and human raters and find that it often generates coherent and feasible 3D object arrangements.

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