Text-to-3D generation has made remarkable progress recently, particularly
with methods based on Score Distillation Sampling (SDS) that leverages
pre-trained 2D diffusion models. While the usage of classifier-free guidance is
well acknowledged to be crucial for successful optimization, it is considered
an auxiliary trick rather than the most essential component. In this paper, we
re-evaluate the role of classifier-free guidance in score distillation and
discover a surprising finding: the guidance alone is enough for effective
text-to-3D generation tasks. We name this method Classifier Score Distillation
(CSD), which can be interpreted as using an implicit classification model for
generation. This new perspective reveals new insights for understanding
existing techniques. We validate the effectiveness of CSD across a variety of
text-to-3D tasks including shape generation, texture synthesis, and shape
editing, achieving results superior to those of state-of-the-art methods. Our
project page is this https URL

通过对分类器无关指导在分数蒸馏中的作用进行重新评估，我们发现了一个令人惊讶的发现：仅仅用指导就足够进行有效的文本到 3D 生成任务。我们称之为分类器分数蒸馏（CSD）方法，它可以理解为使用隐式分类模型进行生成。这种新视角揭示了对现有技术的新见解。我们在形状生成、纹理合成和形状编辑等各种文本到 3D 任务中验证了 CSD 的有效性，取得了优于现有方法的结果。