Text-to-3D with classifier score distillation
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 https://xinyu-andy.github.io/Classifier-Score-Distillation
65409540bd60d2bd192c124e
Xin Yu
admin_assigned
2025-03-12T15:20:36.731Z
62d1a71ac40bde8b986c3748
/avatars/6dbfba55563c71ed0db0d59c2629d525.svg
Xin Yu
user
Andyx
65409540bd60d2bd192c124f
Yuan-Chen Guo
admin_assigned
2023-10-31T10:59:55.703Z
6346aaa3f06b237ba4e297b0
/avatars/5acb986e993eab1461200f3e9d99d022.svg
Yuan-Chen Guo
user
bennyguo
65409540bd60d2bd192c1250
Yangguang Li
admin_assigned
2023-10-31T11:00:10.873Z
644990cbdf4e6cb7eaee0559
/avatars/dbe8d4679a3b29cbb1cb7b3da13ed61f.svg
Yangguang
user
liyangguang
65409540bd60d2bd192c1251
Ding Liang
admin_assigned
2023-10-31T11:00:28.348Z
6476b01cbb7fdd7f425dcefb
/avatars/65efe7067f47a68c204a1ab5a772b939.svg
dingliang
user
dingliang01
65409540bd60d2bd192c1252
Song-Hai Zhang
65409540bd60d2bd192c1253
Xiaojuan Qi
claimed_verified
2025-07-15T19:17:27.537Z
6875266f9cd3191dfddc7071
/avatars/64c581910833b111e9a7bae5b8740229.svg
xiaojuan qi
user
xjqi
Text-to-3D with classifier score distillation
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 https://xinyu-andy.github.io/Classifier-Score-Distillation
Classifier Score Distillation leverages classifier-free guidance as a standalone method for effective text-to-3D generation tasks, outperforming state-of-the-art techniques.
Score Distillation Sampling diffusion models classifier-free guidance Classifier Score Distillation implicit classification model