The development of generative models that create 3D content from a text prompt has made considerable strides thanks to the use of the score distillation sampling (SDS) method on pre-trained diffusion models for image generation. However, the SDS method is also the source of several artifacts, such as the Janus problem, the misalignment between the text prompt and the generated 3D model, and 3D model inaccuracies. While existing methods heavily rely on the qualitative assessment of these artifacts through visual inspection of a limited set of samples, in this work we propose more objective quantitative evaluation metrics, which we cross-validate via human ratings, and show analysis of the failure cases of the SDS technique. We demonstrate the effectiveness of this analysis by designing a novel computationally efficient baseline model that achieves state-of-the-art performance on the proposed metrics while addressing all the above-mentioned artifacts.

通过定量评估指标、交叉验证人类评级以及分析SDS技术的失败案例，我们提出了一种新的计算效率基准模型，以解决生成模型中的艺术问题，包括3D模型准确性和文本提示之间的错位问题，并在所提出的评估指标上达到了最先进的性能。

一种基于得分蒸馏采样的文本到3D的定量评估