Text-to-3D scene generation holds immense potential for the gaming, film, and
architecture sectors. Despite significant progress, existing methods struggle
with maintaining high quality, consistency, and editing flexibility. In this
paper, we propose DreamScene, a 3D Gaussian-based novel text-to-3D scene
generation framework, to tackle the aforementioned three challenges mainly via
two strategies. First, DreamScene employs Formation Pattern Sampling (FPS), a
multi-timestep sampling strategy guided by the formation patterns of 3D
objects, to form fast, semantically rich, and high-quality representations. FPS
uses 3D Gaussian filtering for optimization stability, and leverages
reconstruction techniques to generate plausible textures. Second, DreamScene
employs a progressive three-stage camera sampling strategy, specifically
designed for both indoor and outdoor settings, to effectively ensure
object-environment integration and scene-wide 3D consistency. Last, DreamScene
enhances scene editing flexibility by integrating objects and environments,
enabling targeted adjustments. Extensive experiments validate DreamScene's
superiority over current state-of-the-art techniques, heralding its
wide-ranging potential for diverse applications. Code and demos will be
released at this https URL .

提出 DreamScene，一个基于 3D 高斯模型的文本生成三维场景框架，使用 Formation Pattern Sampling 和三阶段摄像机采样策略解决现有方法中的质量、一致性、编辑灵活性等问题，通过整合对象和环境实现场景编辑的灵活性，并实验证实其在各种应用中的优越性。