title: Taming Mode Collapse in Score Distillation for Text-to-3D Generation
summary: Despite the remarkable performance of score distillation in text-to-3D
  generation, such techniques notoriously suffer from view inconsistency issues,
  also known as "Janus" artifact, where the generated objects fake each view with
  multiple front faces. Although empirically effective methods have approached
  this problem via score debiasing or prompt engineering, a more rigorous
  perspective to explain and tackle this problem remains elusive. In this paper,
  we reveal that the existing score distillation-based text-to-3D generation
  frameworks degenerate to maximal likelihood seeking on each view independently
  and thus suffer from the mode collapse problem, manifesting as the Janus
  artifact in practice. To tame mode collapse, we improve score distillation by
  re-establishing in entropy term in the corresponding variational objective,
  which is applied to the distribution of rendered images. Maximizing the entropy
  encourages diversity among different views in generated 3D assets, thereby
  mitigating the Janus problem. Based on this new objective, we derive a new
  update rule for 3D score distillation, dubbed Entropic Score Distillation
  (ESD). We theoretically reveal that ESD can be simplified and implemented by
  just adopting the classifier-free guidance trick upon variational score
  distillation. Although embarrassingly straightforward, our extensive
  experiments successfully demonstrate that ESD can be an effective treatment for
  Janus artifacts in score distillation.
paper_authors._id: 6594cabd315340de5ff0d1d2
paper_authors.name: Peihao Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-02-09T09:28:32.291Z
paper_authors._id: 62c8f1773e28b8ee4cddca09
paper_authors.avatarUrl: /avatars/9edbcc84e4ab2b335011daab8acfdbb8.svg
paper_authors.fullname: Peihao Wang
paper_authors.type: user
paper_authors.user: peihaowang
paper_authors._id: 6594cabd315340de5ff0d1d3
paper_authors.name: Dejia Xu
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-03T07:06:09.797Z
paper_authors._id: 62f687a0c58915315c4ff75d
paper_authors.avatarUrl: /avatars/b657180c7666735062782edd4f6a69c9.svg
paper_authors.fullname: Dejia Xu
paper_authors.type: user
paper_authors.user: ir1d
paper_authors._id: 6594cabd315340de5ff0d1d4
paper_authors.name: Zhiwen Fan
paper_authors._id: 6594cabd315340de5ff0d1d5
paper_authors.name: Dilin Wang
paper_authors._id: 6594cabd315340de5ff0d1d6
paper_authors.name: Sreyas Mohan
paper_authors._id: 6594cabd315340de5ff0d1d7
paper_authors.name: Forrest Iandola
paper_authors._id: 6594cabd315340de5ff0d1d8
paper_authors.name: Rakesh Ranjan
paper_authors._id: 6594cabd315340de5ff0d1d9
paper_authors.name: Yilei Li
paper_authors._id: 6594cabd315340de5ff0d1da
paper_authors.name: Qiang Liu
paper_authors._id: 6594cabd315340de5ff0d1db
paper_authors.name: Zhangyang Wang
paper_authors._id: 6594cabd315340de5ff0d1dc
paper_authors.name: Vikas Chandra
paper_title: Taming Mode Collapse in Score Distillation for Text-to-3D Generation
paper_summary: Despite the remarkable performance of score distillation in text-to-3D
  generation, such techniques notoriously suffer from view inconsistency issues,
  also known as "Janus" artifact, where the generated objects fake each view with
  multiple front faces. Although empirically effective methods have approached
  this problem via score debiasing or prompt engineering, a more rigorous
  perspective to explain and tackle this problem remains elusive. In this paper,
  we reveal that the existing score distillation-based text-to-3D generation
  frameworks degenerate to maximal likelihood seeking on each view independently
  and thus suffer from the mode collapse problem, manifesting as the Janus
  artifact in practice. To tame mode collapse, we improve score distillation by
  re-establishing in entropy term in the corresponding variational objective,
  which is applied to the distribution of rendered images. Maximizing the entropy
  encourages diversity among different views in generated 3D assets, thereby
  mitigating the Janus problem. Based on this new objective, we derive a new
  update rule for 3D score distillation, dubbed Entropic Score Distillation
  (ESD). We theoretically reveal that ESD can be simplified and implemented by
  just adopting the classifier-free guidance trick upon variational score
  distillation. Although embarrassingly straightforward, our extensive
  experiments successfully demonstrate that ESD can be an effective treatment for
  Janus artifacts in score distillation.
paper_ai_keywords: score distillation text-to-3D generation view inconsistency Janus artifact score debiasing prompt engineering maximal likelihood seeking mode collapse entropy term variational objective Entropic Score Distillation (ESD) classifier-free guidance trick variational score distillation