高效生成无界户外场景，支持多风格融合。
NuiScene: Exploring Efficient Generation of Unbounded Outdoor Scenes
- 将场景切块编码为统一向量集，压缩与性能更优。
- 提出显式外推模型，生成更快且连贯性更强。
- 支持不同风格场景融合，适合大尺度景观生成。
本文探索生成广阔户外场景的任务，涵盖城堡到摩天大楼等多样场景。与以往聚焦室内场景的研究不同，户外场景生成面临高度变化大、需快速生成大范围景观等挑战。为此，我们提出一种高效方法，将场景切块编码为统一向量集，相比之前基于空间结构潜变量的方法具有更好的压缩率和性能。此外，我们训练了一个显式外推模型用于无界生成，相比依赖重采样的内补方法，在保持连贯性的同时还避免了额外的扩散步骤，提升了生成速度。为支持该任务，我们构建了NuiScene43数据集，一个小型但高质量的场景集合，已预处理用于联合训练。值得注意的是，当在多种风格场景上训练时，我们的模型能在一个场景中自然融合乡村住宅与城市高楼，展现了数据集设计对异质场景联合训练的潜力。
原文摘要 · Abstract (English)
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.
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