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arXiv · 2503.16420

SynCity: Training-Free Generation of 3D Worlds

Paul Engstler·Aleksandar Shtedritski·Iro Laina·Christian Rupprecht·Andrea Vedaldi
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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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Paul Engstler, Aleksandar Shtedritski, Iro Laina, Christian Rupprecht, Andrea Vedaldi. 2025-03-20. SynCity: Training-Free Generation of 3D Worlds. https://arxiv.org/abs/2503.16420

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