2025 ICCV ICCV 2025

SynCity: Training-Free Generation of 3D Worlds

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.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Computer Vision and Deep Learning
🧭 Keyword Pioneer — 3d world generation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio