Designing 3D scenes is traditionally a challenging and laborious task that demands both artistic expertise and proficiency with complex software.
Recent advances in text-to-3D generation have greatly simplified this process by letting users create scenes based on simple text descriptions.
However, as these methods generally require extra training or in-context learning, their performance is often hindered by the limited availability of high-quality 3D data.
In contrast, modern text-to-image models learned from web-scale images can generate scenes with diverse, reliable spatial layouts and consistent, visually appealing styles.
Our key insight is that instead of learning directly from 3D scenes, we can leverage generated 2D images as an intermediary to guide 3D synthesis.
In light of this, we introduce ArtiScene, a training-free automated pipeline for scene design that integrates the flexibility of free-form text-to-image generation with the diversity and reliability of 2D intermediary layouts.
We generate the 2D intermediary image from a scene description, extract object shapes and appearances, create 3D models, and assemble them into the final scene with geometry, position, and pose extracted from the same image.
Being generalizable to a wide range of scenes and styles, ArtiScene is shown to outperform state-of-the-art benchmarks by a large margin in layout and aesthetic quality by quantitative metrics.
It also averages a 74.89% winning rate in extensive user studies and 95.07% in GPT evaluation.
