title: DreamDPO: Aligning Text-to-3D Generation with Human Preferences via
    Direct Preference Optimization
summary: Text-to-3D generation automates 3D content creation from textual
  descriptions, which offers transformative potential across various fields.
  However, existing methods often struggle to align generated content with human
  preferences, limiting their applicability and flexibility. To address these
  limitations, in this paper, we propose DreamDPO, an optimization-based
  framework that integrates human preferences into the 3D generation process,
  through direct preference optimization. Practically, DreamDPO first constructs
  pairwise examples, then compare their alignment with human preferences using
  reward or large multimodal models, and lastly optimizes the 3D representation
  with a preference-driven loss function. By leveraging pairwise comparison to
  reflect preferences, DreamDPO reduces reliance on precise pointwise quality
  evaluations while enabling fine-grained controllability through
  preference-guided optimization. Experiments demonstrate that DreamDPO achieves
  competitive results, and provides higher-quality and more controllable 3D
  content compared to existing methods. The code and models will be open-sourced.
paper_authors._id: 67aafd90141fac22732a79b3
paper_authors.name: Zhenglin Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-11T15:53:27.764Z
paper_authors._id: 6425318d175bd2952281065e
paper_authors.avatarUrl: /avatars/37deb6ceb1552dece43a1c8c13c1c871.svg
paper_authors.fullname: ZhenglinZhou
paper_authors.type: user
paper_authors.user: zhenglin
paper_authors._id: 67aafd90141fac22732a79b4
paper_authors.name: Xiaobo Xia
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-11T15:53:52.391Z
paper_authors._id: 66c48f944511077b9ff5ce9d
paper_authors.avatarUrl: /avatars/a4977246bc951e9da0cb2301bedd8249.svg
paper_authors.fullname: Xiaobo Xia
paper_authors.type: user
paper_authors.user: XiaoboXia1997
paper_authors._id: 67aafd90141fac22732a79b5
paper_authors.name: Fan Ma
paper_authors._id: 67aafd90141fac22732a79b6
paper_authors.name: Hehe Fan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-11T15:54:14.222Z
paper_authors._id: 64ad04020fb9b20dbabbd30e
paper_authors.avatarUrl: /avatars/a6bae4a3a4bcd6b54c33860fe14c7923.svg
paper_authors.fullname: Hehe Fan
paper_authors.type: user
paper_authors.user: hehefan
paper_authors._id: 67aafd90141fac22732a79b7
paper_authors.name: Yi Yang
paper_authors._id: 67aafd90141fac22732a79b8
paper_authors.name: Tat-Seng Chua
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-11T15:54:04.147Z
paper_authors._id: 6570ae84c4993b8fb96f41a8
paper_authors.avatarUrl: /avatars/21f7d79d46ac4df0ecff8eca7678b33f.svg
paper_authors.fullname: Tat-Seng Chua
paper_authors.type: user
paper_authors.user: chuats
paper_title: DreamDPO: Aligning Text-to-3D Generation with Human Preferences via
    Direct Preference Optimization
paper_summary: Text-to-3D generation automates 3D content creation from textual
  descriptions, which offers transformative potential across various fields.
  However, existing methods often struggle to align generated content with human
  preferences, limiting their applicability and flexibility. To address these
  limitations, in this paper, we propose DreamDPO, an optimization-based
  framework that integrates human preferences into the 3D generation process,
  through direct preference optimization. Practically, DreamDPO first constructs
  pairwise examples, then compare their alignment with human preferences using
  reward or large multimodal models, and lastly optimizes the 3D representation
  with a preference-driven loss function. By leveraging pairwise comparison to
  reflect preferences, DreamDPO reduces reliance on precise pointwise quality
  evaluations while enabling fine-grained controllability through
  preference-guided optimization. Experiments demonstrate that DreamDPO achieves
  competitive results, and provides higher-quality and more controllable 3D
  content compared to existing methods. The code and models will be open-sourced.
paper_ai_keywords: optimization-based framework direct preference optimization pairwise examples reward models large multimodal models preference-driven loss function preference-guided optimization