title: DreamPolisher: Towards High-Quality Text-to-3D Generation via Geometric
    Diffusion
summary: We present DreamPolisher, a novel Gaussian Splatting based method with
  geometric guidance, tailored to learn cross-view consistency and intricate
  detail from textual descriptions. While recent progress on text-to-3D
  generation methods have been promising, prevailing methods often fail to ensure
  view-consistency and textural richness. This problem becomes particularly
  noticeable for methods that work with text input alone. To address this, we
  propose a two-stage Gaussian Splatting based approach that enforces geometric
  consistency among views. Initially, a coarse 3D generation undergoes refinement
  via geometric optimization. Subsequently, we use a ControlNet driven refiner
  coupled with the geometric consistency term to improve both texture fidelity
  and overall consistency of the generated 3D asset. Empirical evaluations across
  diverse textual prompts spanning various object categories demonstrate the
  efficacy of DreamPolisher in generating consistent and realistic 3D objects,
  aligning closely with the semantics of the textual instructions.
paper_authors._id: 6603982c9b248dec28e972c2
paper_authors.name: Yuanze Lin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-27T09:55:07.270Z
paper_authors._id: 633be9e4c0fb6fd232ee679d
paper_authors.avatarUrl: /avatars/04984852e6d139db5975015d1a7c9d5f.svg
paper_authors.fullname: Yuanze Lin
paper_authors.type: user
paper_authors.user: Yuanze
paper_authors._id: 6603982c9b248dec28e972c3
paper_authors.name: Ronald Clark
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-27T09:55:22.892Z
paper_authors._id: 6305ee63d70693fdf1c7dbb8
paper_authors.avatarUrl: /avatars/0e81ed3757b4e65be82063b538c3fe49.svg
paper_authors.fullname: Ronald Clark
paper_authors.type: user
paper_authors.user: r0nn13
paper_authors._id: 6603982c9b248dec28e972c4
paper_authors.name: Philip Torr
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-27T09:55:33.385Z
paper_authors._id: 6565ed28a5ec0231cb07225f
paper_authors.avatarUrl: /avatars/7f95bba9aa7811d56eecb380827abfac.svg
paper_authors.fullname: prof philip torr
paper_authors.type: user
paper_authors.user: philiptorr
paper_title: DreamPolisher: Towards High-Quality Text-to-3D Generation via Geometric
    Diffusion
paper_summary: We present DreamPolisher, a novel Gaussian Splatting based method with
  geometric guidance, tailored to learn cross-view consistency and intricate
  detail from textual descriptions. While recent progress on text-to-3D
  generation methods have been promising, prevailing methods often fail to ensure
  view-consistency and textural richness. This problem becomes particularly
  noticeable for methods that work with text input alone. To address this, we
  propose a two-stage Gaussian Splatting based approach that enforces geometric
  consistency among views. Initially, a coarse 3D generation undergoes refinement
  via geometric optimization. Subsequently, we use a ControlNet driven refiner
  coupled with the geometric consistency term to improve both texture fidelity
  and overall consistency of the generated 3D asset. Empirical evaluations across
  diverse textual prompts spanning various object categories demonstrate the
  efficacy of DreamPolisher in generating consistent and realistic 3D objects,
  aligning closely with the semantics of the textual instructions.
paper_ai_keywords: Gaussian Splatting geometric guidance cross-view consistency textual descriptions text-to-3D generation geometric consistency coarse 3D generation refinement geometric optimization ControlNet texture fidelity semantic instructions