  Witnessing the evolution of text-to-image diffusion models, significant
strides have been made in text-to-3D generation. Currently, two primary
paradigms dominate the field of text-to-3D: the feed-forward generation
solutions, capable of swiftly producing 3D assets but often yielding coarse
results, and the Score Distillation Sampling (SDS) based solutions, known for
generating high-fidelity 3D assets albeit at a slower pace. The synergistic
integration of these methods holds substantial promise for advancing 3D
generation techniques. In this paper, we present BoostDream, a highly efficient
plug-and-play 3D refining method designed to transform coarse 3D assets into
high-quality. The BoostDream framework comprises three distinct processes: (1)
We introduce 3D model distillation that fits differentiable representations
from the 3D assets obtained through feed-forward generation. (2) A novel
multi-view SDS loss is designed, which utilizes a multi-view aware 2D diffusion
model to refine the 3D assets. (3) We propose to use prompt and multi-view
consistent normal maps as guidance in refinement.Our extensive experiment is
conducted on different differentiable 3D representations, revealing that
BoostDream excels in generating high-quality 3D assets rapidly, overcoming the
Janus problem compared to conventional SDS-based methods. This breakthrough
signifies a substantial advancement in both the efficiency and quality of 3D
generation processes.
