title: Stable Score Distillation for High-Quality 3D Generation
summary: Score Distillation Sampling (SDS) has exhibited remarkable performance in
  conditional 3D content generation. However, a comprehensive understanding of
  the SDS formulation is still lacking, hindering the development of 3D
  generation. In this work, we present an interpretation of SDS as a combination
  of three functional components: mode-disengaging, mode-seeking and
  variance-reducing terms, and analyze the properties of each. We show that
  problems such as over-smoothness and color-saturation result from the intrinsic
  deficiency of the supervision terms and reveal that the variance-reducing term
  introduced by SDS is sub-optimal. Additionally, we shed light on the adoption
  of large Classifier-Free Guidance (CFG) scale for 3D generation. Based on the
  analysis, we propose a simple yet effective approach named Stable Score
  Distillation (SSD) which strategically orchestrates each term for high-quality
  3D generation. Extensive experiments validate the efficacy of our approach,
  demonstrating its ability to generate high-fidelity 3D content without
  succumbing to issues such as over-smoothness and over-saturation, even under
  low CFG conditions with the most challenging NeRF representation.
paper_authors._id: 657fd0d1eaad53ff67ef9f9f
paper_authors.name: Boshi Tang
paper_authors._id: 657fd0d1eaad53ff67ef9fa0
paper_authors.name: Jianan Wang
paper_authors._id: 657fd0d1eaad53ff67ef9fa1
paper_authors.name: Zhiyong Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-18T11:28:22.095Z
paper_authors._id: 6487e877b541258e038ea921
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/jXU7VV9V7_X530IOOCvVZ.jpeg
paper_authors.fullname: wuzhiyong
paper_authors.type: user
paper_authors.user: zhiyongdezhi
paper_authors._id: 657fd0d1eaad53ff67ef9fa2
paper_authors.name: Lei Zhang
paper_title: Stable Score Distillation for High-Quality 3D Generation
paper_summary: Score Distillation Sampling (SDS) has exhibited remarkable performance in
  conditional 3D content generation. However, a comprehensive understanding of
  the SDS formulation is still lacking, hindering the development of 3D
  generation. In this work, we present an interpretation of SDS as a combination
  of three functional components: mode-disengaging, mode-seeking and
  variance-reducing terms, and analyze the properties of each. We show that
  problems such as over-smoothness and color-saturation result from the intrinsic
  deficiency of the supervision terms and reveal that the variance-reducing term
  introduced by SDS is sub-optimal. Additionally, we shed light on the adoption
  of large Classifier-Free Guidance (CFG) scale for 3D generation. Based on the
  analysis, we propose a simple yet effective approach named Stable Score
  Distillation (SSD) which strategically orchestrates each term for high-quality
  3D generation. Extensive experiments validate the efficacy of our approach,
  demonstrating its ability to generate high-fidelity 3D content without
  succumbing to issues such as over-smoothness and over-saturation, even under
  low CFG conditions with the most challenging NeRF representation.
paper_ai_summary: The paper interprets Score Distillation Sampling as a combination of mode-disengaging, mode-seeking, and variance-reducing terms to improve 3D content generation by proposing a Stable Score Distillation approach, addressing issues like over-smoothness and color-saturation.
paper_ai_keywords: Score Distillation Sampling mode-disengaging mode-seeking variance-reducing Classifier-Free Guidance Stable Score Distillation NeRF representation