标题： 生成360°视频是您对3D场景所需的内容 显示英文标题

标题： Generating 360° Video is What You Need For a 3D Scene

摘要： 生成3D场景仍然是一个具有挑战性的任务，这是由于缺乏现成的场景数据。 大多数现有方法仅能生成部分场景，并提供有限的导航自由度。 我们引入了一种实用且可扩展的解决方案，使用360{\deg }视频作为中间场景表示，捕捉完整的场景上下文并确保生成过程中的视觉内容一致性。 我们提出了WorldPrompter，一个从文本提示合成可行走的3D场景的生成管道。 WorldPrompter结合了一个条件360{\deg }全景视频生成器，能够生成一个包含128帧的视频，模拟一个人穿过并捕捉虚拟环境的过程。 随后，该视频由一个快速的前馈3D重建器重建为高斯点云，从而在3D场景中实现真正的可行走体验。 实验表明，我们的全景视频生成模型，在图像和视频数据混合训练下，对于静态场景实现了令人信服的空间和时间一致性。 这一点通过平均COLMAP匹配率94.6%得到验证，使得高质量的全景高斯点云重建成为可能，并提升了整个场景的导航性能。 定性和定量结果也表明，它优于最先进的360{\deg }视频生成器和3D场景生成模型。 显示英文摘要

摘要： Generating 3D scenes is still a challenging task due to the lack of readily available scene data. Most existing methods only produce partial scenes and provide limited navigational freedom. We introduce a practical and scalable solution that uses 360{\deg} video as an intermediate scene representation, capturing the full-scene context and ensuring consistent visual content throughout the generation. We propose WorldPrompter, a generative pipeline that synthesizes traversable 3D scenes from text prompts. WorldPrompter incorporates a conditional 360{\deg} panoramic video generator, capable of producing a 128-frame video that simulates a person walking through and capturing a virtual environment. The resulting video is then reconstructed as Gaussian splats by a fast feedforward 3D reconstructor, enabling a true walkable experience within the 3D scene. Experiments demonstrate that our panoramic video generation model, trained with a mix of image and video data, achieves convincing spatial and temporal consistency for static scenes. This is validated by an average COLMAP matching rate of 94.6\%, allowing for high-quality panoramic Gaussian splat reconstruction and improved navigation throughout the scene. Qualitative and quantitative results also show it outperforms the state-of-the-art 360{\deg} video generators and 3D scene generation models.