  Although recent advancements in text-to-3D generation have significantly
improved generation quality, issues like limited level of detail and low
fidelity still persist, which requires further improvement. To understand the
essence of those issues, we thoroughly analyze current score distillation
methods by connecting theories of consistency distillation to score
distillation. Based on the insights acquired through analysis, we propose an
optimization framework, Guided Consistency Sampling (GCS), integrated with 3D
Gaussian Splatting (3DGS) to alleviate those issues. Additionally, we have
observed the persistent oversaturation in the rendered views of generated 3D
assets. From experiments, we find that it is caused by unwanted accumulated
brightness in 3DGS during optimization. To mitigate this issue, we introduce a
Brightness-Equalized Generation (BEG) scheme in 3DGS rendering. Experimental
results demonstrate that our approach generates 3D assets with more details and
higher fidelity than state-of-the-art methods. The codes are released at
https://github.com/LMozart/ECCV2024-GCS-BEG.
