SynCity: Training-Free Generation of 3D Worlds

• URL: http://arxiv.org/abs/2503.16420v1
• Date: Thu, 20 Mar 2025 17:59:40 GMT
• Title: SynCity: Training-Free Generation of 3D Worlds
• Authors: Paul Engstler, Aleksandar Shtedritski, Iro Laina, Christian Rupprecht, Andrea Vedaldi,
• Abstract summary: We propose SynCity, a training- and optimization-free approach to generating 3D worlds from textual descriptions.<n>We show how 3D and 2D generators can be combined to generate ever-expanding scenes.
• Score: 107.69875149880679
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We address the challenge of generating 3D worlds from textual descriptions. We propose SynCity, a training- and optimization-free approach, which leverages the geometric precision of pre-trained 3D generative models and the artistic versatility of 2D image generators to create large, high-quality 3D spaces. While most 3D generative models are object-centric and cannot generate large-scale worlds, we show how 3D and 2D generators can be combined to generate ever-expanding scenes. Through a tile-based approach, we allow fine-grained control over the layout and the appearance of scenes. The world is generated tile-by-tile, and each new tile is generated within its world-context and then fused with the scene. SynCity generates compelling and immersive scenes that are rich in detail and diversity.

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