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.
@article{arxiv.2503.16420,
title = {SynCity: Training-Free Generation of 3D Worlds},
author = {Paul Engstler and Aleksandar Shtedritski and Iro Laina and Christian Rupprecht and Andrea Vedaldi},
journal= {arXiv preprint arXiv:2503.16420},
year = {2025}
}