title: NuiScene: Exploring Efficient Generation of Unbounded Outdoor Scenes
summary: In this paper, we explore the task of generating expansive outdoor scenes,
  ranging from castles to high-rises. Unlike indoor scene generation, which has
  been a primary focus of prior work, outdoor scene generation presents unique
  challenges, including wide variations in scene heights and the need for a
  method capable of rapidly producing large landscapes. To address this, we
  propose an efficient approach that encodes scene chunks as uniform vector sets,
  offering better compression and performance than the spatially structured
  latents used in prior methods. Furthermore, we train an explicit outpainting
  model for unbounded generation, which improves coherence compared to prior
  resampling-based inpainting schemes while also speeding up generation by
  eliminating extra diffusion steps. To facilitate this task, we curate
  NuiScene43, a small but high-quality set of scenes, preprocessed for joint
  training. Notably, when trained on scenes of varying styles, our model can
  blend different environments, such as rural houses and city skyscrapers, within
  the same scene, highlighting the potential of our curation process to leverage
  heterogeneous scenes for joint training.
paper_authors._id: 67dd269625f9991caf94c667
paper_authors.name: Han-Hung Lee
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-21T11:37:43.579Z
paper_authors._id: 6313c7a754e6e5d9f0fa4d81
paper_authors.avatarUrl: /avatars/0b0e5f09171b87d61260225b2021aa98.svg
paper_authors.fullname: HAN-HUNG LEE
paper_authors.type: user
paper_authors.user: rexleeppp
paper_authors._id: 67dd269625f9991caf94c668
paper_authors.name: Qinghong Han
paper_authors._id: 67dd269625f9991caf94c669
paper_authors.name: Angel X. Chang
paper_title: NuiScene: Exploring Efficient Generation of Unbounded Outdoor Scenes
paper_summary: In this paper, we explore the task of generating expansive outdoor scenes,
  ranging from castles to high-rises. Unlike indoor scene generation, which has
  been a primary focus of prior work, outdoor scene generation presents unique
  challenges, including wide variations in scene heights and the need for a
  method capable of rapidly producing large landscapes. To address this, we
  propose an efficient approach that encodes scene chunks as uniform vector sets,
  offering better compression and performance than the spatially structured
  latents used in prior methods. Furthermore, we train an explicit outpainting
  model for unbounded generation, which improves coherence compared to prior
  resampling-based inpainting schemes while also speeding up generation by
  eliminating extra diffusion steps. To facilitate this task, we curate
  NuiScene43, a small but high-quality set of scenes, preprocessed for joint
  training. Notably, when trained on scenes of varying styles, our model can
  blend different environments, such as rural houses and city skyscrapers, within
  the same scene, highlighting the potential of our curation process to leverage
  heterogeneous scenes for joint training.
paper_ai_summary: An efficient approach is proposed for outdoor scene generation using uniform vector sets for scene chunk encoding and an explicit outpainting model for coherent, rapid landscape generation.
paper_ai_keywords: vector sets spatially structured latents outpainting model diffusion steps