title: Constructing a 3D Town from a Single Image
summary: 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.
paper_authors._id: 682e85aa7b41e70cf11758b6
paper_authors.name: Kaizhi Zheng
paper_authors._id: 682e85aa7b41e70cf11758b7
paper_authors.name: Ruijian Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:22:21.394Z
paper_authors._id: 67f0985552f32a539aa00cb2
paper_authors.avatarUrl: /avatars/4337511d15223342e2f2b150cbdcf2e7.svg
paper_authors.fullname: RUIJIAN ZHA
paper_authors.type: user
paper_authors.user: rz2689
paper_authors._id: 682e85aa7b41e70cf11758b8
paper_authors.name: Jing Gu
paper_authors._id: 682e85aa7b41e70cf11758b9
paper_authors.name: Jie Yang
paper_authors._id: 682e85aa7b41e70cf11758ba
paper_authors.name: Xin Eric Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-26T08:22:23.701Z
paper_authors._id: 64679a226192d39142245e5e
paper_authors.avatarUrl: /avatars/05abee0b6317f100923936ca2099e9eb.svg
paper_authors.fullname: Xin Eric Wang
paper_authors.type: user
paper_authors.user: xw-eric
paper_title: Constructing a 3D Town from a Single Image
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 3D generative models 3DTown region-based generation spatial-aware 3D inpainting masked rectified flow Trellis Hunyuan3D-2 TripoSG