SynCity: Training-Free Generation of 3D Worlds
Authors: name: Paul Engstler; name: Aleksandar Shtedritski; name: Iro Laina; name: Christian Rupprecht; name: Andrea Vedaldi
Published: 2025-03-20 17:59:40

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.