ICCV 2025poster0 citations
SynCity: Training-Free Generation of 3D Worlds
Paul Engstler, Aleksandar Shtedritski, Iro Laina, Christian Rupprecht, Andrea Vedaldi
Abstract
We propose SynCity, a method for generating explorable 3D worlds from textual descriptions. Our approach leverages pre-trained textual, image, and 3D generators without requiring fine-tuning or inference-time optimization. While most 3D generators are object-centric and unable to create large-scale worlds, we demonstrate how 2D and 3D generators can be combined to produce ever-expanding scenes. The world is generated tile by tile, with each new tile created within its context and seamlessly integrated into the scene. SynCity enables fine-grained control over the appearance and layout of the generated worlds, which are both detailed and diverse.
BibTeX
@InProceedings{Engstler_2025_ICCV,
author = {Engstler, Paul and Shtedritski, Aleksandar and Laina, Iro and Rupprecht, Christian and Vedaldi, Andrea},
title = {SynCity: Training-Free Generation of 3D Worlds},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2025},
pages = {27585-27595}
}