标题： DreamAnywhere：以物体为中心的全景3D场景生成 显示英文标题

标题： DreamAnywhere: Object-Centric Panoramic 3D Scene Generation

摘要： 最近在文本到3D场景生成方面的进展展示了显著的潜力，可以改变多个行业的内容创作方式。 尽管研究界在解决这一复杂任务的挑战方面取得了令人印象深刻的进展，但现有方法生成的环境通常只面向前方，缺乏视觉保真度，表现出有限的场景理解，并且通常仅针对室内或室外设置进行微调。 在本工作中，我们解决了这些问题，并提出了DreamAnywhere，这是一个用于快速生成和原型设计3D场景的模块化系统。 我们的系统从文本合成一个360{\deg }全景图像，将其分解为背景和物体，通过混合修复构建完整的3D表示，并将物体掩码提升为详细3D物体并放置在虚拟环境中。 DreamAnywhere支持沉浸式导航和直观的对象级编辑，使其非常适合场景探索、视觉样稿和快速原型设计——所有这些只需最少的手动建模。 这些特性使我们的系统特别适合低成本电影制作，可以在不涉及传统3D工作流程开销的情况下快速迭代场景布局和视觉基调。 我们的模块化流程高度可定制，因为它允许独立替换组件。 与当前最先进的基于文本和图像的3D场景生成方法相比， DreamAnywhere在新视角合成的一致性方面表现出显著改进，并实现了具有竞争力的图像质量，证明了其在多样且具有挑战性的场景中的有效性。 一项全面的用户研究表明，我们的方法明显优于现有方法，验证了其技术稳健性和实际实用性。 显示英文摘要

摘要： Recent advances in text-to-3D scene generation have demonstrated significant potential to transform content creation across multiple industries. Although the research community has made impressive progress in addressing the challenges of this complex task, existing methods often generate environments that are only front-facing, lack visual fidelity, exhibit limited scene understanding, and are typically fine-tuned for either indoor or outdoor settings. In this work, we address these issues and propose DreamAnywhere, a modular system for the fast generation and prototyping of 3D scenes. Our system synthesizes a 360{\deg} panoramic image from text, decomposes it into background and objects, constructs a complete 3D representation through hybrid inpainting, and lifts object masks to detailed 3D objects that are placed in the virtual environment. DreamAnywhere supports immersive navigation and intuitive object-level editing, making it ideal for scene exploration, visual mock-ups, and rapid prototyping -- all with minimal manual modeling. These features make our system particularly suitable for low-budget movie production, enabling quick iteration on scene layout and visual tone without the overhead of traditional 3D workflows. Our modular pipeline is highly customizable as it allows components to be replaced independently. Compared to current state-of-the-art text and image-based 3D scene generation approaches, DreamAnywhere shows significant improvements in coherence in novel view synthesis and achieves competitive image quality, demonstrating its effectiveness across diverse and challenging scenarios. A comprehensive user study demonstrates a clear preference for our method over existing approaches, validating both its technical robustness and practical usefulness.