NuiScene: Exploring Efficient Generation of Unbounded Outdoor Scenes
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.
67dd269625f9991caf94c667
Han-Hung Lee
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2025-03-21T11:37:43.579Z
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/avatars/0b0e5f09171b87d61260225b2021aa98.svg
HAN-HUNG LEE
user
rexleeppp
67dd269625f9991caf94c668
Qinghong Han
67dd269625f9991caf94c669
Angel X. Chang
NuiScene: Exploring Efficient Generation of Unbounded Outdoor Scenes
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.
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.
vector sets spatially structured latents outpainting model diffusion steps