Layout-your-3D: Controllable and Precise 3D Generation with 2D Blueprint

Junwei Zhou, Xueting Li, Lu Qi, Ming-Hsuan Yang

Keywords: 3D generation, gaussian splatting, Text-to-3D, compositional asset generation

TL;DR: Given a text prompt describing multiple objects and their spatial relationships, our method generates a 3D scene depicting these objects naturally interacting with one another.

Abstract: We present Layout-Your-3D, a framework that allows controllable and compositional 3D generation from text prompts. Existing text-to-3D methods often struggle to generate assets with plausible object interactions or require tedious optimization processes. To address these challenges, our approach leverages 2D layouts as a blueprint to facilitate precise and plausible control over 3D generation. Starting with a 2D layout provided by a user or generated from a text description, we first create a coarse 3D scene using a carefully designed initialization process based on efficient reconstruction models. To enforce coherent global 3D layouts and enhance the quality of instance appearances, we propose a collision-aware layout optimization process followed by instance-wise refinement. Experimental results demonstrate that Layout-Your-3D yields more reasonable and visually appealing compositional 3D assets while significantly reducing the time required for each prompt. Additionally, Layout-Your-3D can be easily applicable to downstream tasks, such as 3D editing and object insertion.

Primary Area: generative models

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Submission Number: 2873