A training-free framework named 3DTown generates realistic 3D scenes from a single top-down image using region-based generation and spatial-aware 3D inpainting techniques.
Acquiring detailed 3D scenes typically demands costly equipment, multi-view
data, or labor-intensive modeling. Therefore, a lightweight alternative,
generating complex 3D scenes from a single top-down image, plays an essential
role in real-world applications. While recent 3D generative models have
achieved remarkable results at the object level, their extension to full-scene
generation often leads to inconsistent geometry, layout hallucinations, and
low-quality meshes. In this work, we introduce 3DTown, a training-free
framework designed to synthesize realistic and coherent 3D scenes from a single
top-down view. Our method is grounded in two principles: region-based
generation to improve image-to-3D alignment and resolution, and spatial-aware
3D inpainting to ensure global scene coherence and high-quality geometry
generation. Specifically, we decompose the input image into overlapping regions
and generate each using a pretrained 3D object generator, followed by a masked
rectified flow inpainting process that fills in missing geometry while
maintaining structural continuity. This modular design allows us to overcome
resolution bottlenecks and preserve spatial structure without requiring 3D
supervision or fine-tuning. Extensive experiments across diverse scenes show
that 3DTown outperforms state-of-the-art baselines, including Trellis,
Hunyuan3D-2, and TripoSG, in terms of geometry quality, spatial coherence, and
texture fidelity. Our results demonstrate that high-quality 3D town generation
is achievable from a single image using a principled, training-free approach.