title: Text-to-3D with classifier score distillation
summary: 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
paper_authors._id: 65409540bd60d2bd192c124e
paper_authors.name: Xin Yu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-12T15:20:36.731Z
paper_authors._id: 62d1a71ac40bde8b986c3748
paper_authors.avatarUrl: /avatars/6dbfba55563c71ed0db0d59c2629d525.svg
paper_authors.fullname: Xin Yu
paper_authors.type: user
paper_authors.user: Andyx
paper_authors._id: 65409540bd60d2bd192c124f
paper_authors.name: Yuan-Chen Guo
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-31T10:59:55.703Z
paper_authors._id: 6346aaa3f06b237ba4e297b0
paper_authors.avatarUrl: /avatars/5acb986e993eab1461200f3e9d99d022.svg
paper_authors.fullname: Yuan-Chen Guo
paper_authors.type: user
paper_authors.user: bennyguo
paper_authors._id: 65409540bd60d2bd192c1250
paper_authors.name: Yangguang Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-31T11:00:10.873Z
paper_authors._id: 644990cbdf4e6cb7eaee0559
paper_authors.avatarUrl: /avatars/dbe8d4679a3b29cbb1cb7b3da13ed61f.svg
paper_authors.fullname: Yangguang
paper_authors.type: user
paper_authors.user: liyangguang
paper_authors._id: 65409540bd60d2bd192c1251
paper_authors.name: Ding Liang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-10-31T11:00:28.348Z
paper_authors._id: 6476b01cbb7fdd7f425dcefb
paper_authors.avatarUrl: /avatars/65efe7067f47a68c204a1ab5a772b939.svg
paper_authors.fullname: dingliang
paper_authors.type: user
paper_authors.user: dingliang01
paper_authors._id: 65409540bd60d2bd192c1252
paper_authors.name: Song-Hai Zhang
paper_authors._id: 65409540bd60d2bd192c1253
paper_authors.name: Xiaojuan Qi
paper_title: Text-to-3D with classifier score distillation
paper_summary: 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
paper_ai_keywords: Score Distillation Sampling (SDS) pre-trained 2D diffusion models classifier-free guidance Classifier Score Distillation (CSD) implicit classification model shape generation texture synthesis shape editing text-to-3D generation tasks state-of-the-art methods