SceneWiz3D: Towards Text-guided 3D Scene Composition
Qihang Zhang Chaoyang Wang Aliaksandr Siarohin Peiye Zhuang Yinghao Xu Ceyuan Yang Dahua Lin Bolei Zhou Sergey Tulyakov Hsin-Ying Lee
We are witnessing significant breakthroughs in the technology for generating
3D objects from text. Existing approaches either leverage large text-to-image
models to optimize a 3D representation or train 3D generators on object-centric
datasets. Generating entire scenes, however, remains very challenging as a
scene contains multiple 3D objects, diverse and scattered. In this work, we
introduce SceneWiz3D, a novel approach to synthesize high-fidelity 3D scenes
from text. We marry the locality of objects with globality of scenes by
introducing a hybrid 3D representation: explicit for objects and implicit for
scenes. Remarkably, an object, being represented explicitly, can be either
generated from text using conventional text-to-3D approaches, or provided by
users. To configure the layout of the scene and automatically place objects, we
apply the Particle Swarm Optimization technique during the optimization
process. Furthermore, it is difficult for certain parts of the scene (e.g.,
corners, occlusion) to receive multi-view supervision, leading to inferior
geometry. We incorporate an RGBD panorama diffusion model to mitigate it,
resulting in high-quality geometry. Extensive evaluation supports that our
approach achieves superior quality over previous approaches, enabling the
generation of detailed and view-consistent 3D scenes.