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Text-to-3D generation has made remarkable progress recently, particularlywith methods based on Score Distillation Sampling (SDS) that leveragespre-trained 2D diffusion models. While the usage of classifier-free guidance iswell acknowledged to be crucial for successful optimization, it is consideredan auxiliary trick rather than the most essential component. In this paper, were-evaluate the role of classifier-free guidance in score distillation anddiscover a surprising finding: the guidance alone is enough for effectivetext-to-3D generation tasks. We name this method Classifier Score Distillation(CSD), which can be interpreted as using an implicit classification model forgeneration. This new perspective reveals new insights for understandingexisting techniques. We validate the effectiveness of CSD across a variety oftext-to-3D tasks including shape generation, texture synthesis, and shapeediting, achieving results superior to those of state-of-the-art methods. Ourproject page is https://xinyu-andy.github.io/Classifier-Score-Distillation

Text-to-3D with Classifier Score Distillation