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arXiv 2401.13203cs.CV

具有解耦对象的风格一致3D室内场景合成

Style-Consistent 3D Indoor Scene Synthesis with Decoupled Objects

Nanyang Technological University(南洋理工大学)

Yunfan Zhang, Hong Huang, Zhiwei Xiong, Zhiqi Shen, Guosheng Lin, Hao Wang, Nicholas Vun

AI总结:

针对现有方法只能整体风格化、无法分离定制对象的问题,提出利用专业边界框放置对象并保持跨对象风格一致的3D室内场景合成流程,可依据自然语言生成真实、多视角一致且多样的场景。

摘要:

可控的3D室内场景合成处于技术进步的前沿,可提供游戏、电影以及增强/虚拟现实等多种应用。在这些场景中对对象进行风格化和解耦的能力是一个关键因素,可在整个编辑过程中提供高级别的控制。这种控制不仅延伸到操作平移和缩放等几何属性,还包括管理外观,例如风格化。当前的场景风格化方法仅限于将风格应用于整个场景,而无法分离和定制单个对象。为应对这一挑战的复杂之处,我们提出了一种独特的、用于合成3D室内场景的流程。我们的方法利用来自专业设计的边界框的信息,在场景中有策略地放置对象。值得注意的是,我们的流程优先保持场景中多个对象之间的风格一致性,从而确保获得与预期美学相符的连贯且具有视觉吸引力的结果。我们流程的核心优势在于,它能够生成不仅在视觉上令人印象深刻,而且展现出照片级真实感、多视角一致性和多样性等特征的3D场景。这些场景是根据各种自然语言提示精心构建的,展示了我们模型的多功能性和适应性。

英文摘要:

Controllable 3D indoor scene synthesis stands at the forefront of technological progress, offering various applications like gaming, film, and augmented/virtual reality. The capability to stylize and de-couple objects within these scenarios is a crucial factor, providing an advanced level of control throughout the editing process. This control extends not just to manipulating geometric attributes like translation and scaling but also includes managing appearances, such as stylization. Current methods for scene stylization are limited to applying styles to the entire scene, without the ability to separate and customize individual objects. Addressing the intricacies of this challenge, we introduce a unique pipeline designed for synthesis 3D indoor scenes. Our approach involves strategically placing objects within the scene, utilizing information from professionally designed bounding boxes. Significantly, our pipeline prioritizes maintaining style consistency across multiple objects within the scene, ensuring a cohesive and visually appealing result aligned with the desired aesthetic. The core strength of our pipeline lies in its ability to generate 3D scenes that are not only visually impressive but also exhibit features like photorealism, multi-view consistency, and diversity. These scenes are crafted in response to various natural language prompts, demonstrating the versatility and adaptability of our model.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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