title: Repaint123: Fast and High-quality One Image to 3D Generation with
    Progressive Controllable 2D Repainting
summary: Recent one image to 3D generation methods commonly adopt Score Distillation
  Sampling (SDS). Despite the impressive results, there are multiple deficiencies
  including multi-view inconsistency, over-saturated and over-smoothed textures,
  as well as the slow generation speed. To address these deficiencies, we present
  Repaint123 to alleviate multi-view bias as well as texture degradation and
  speed up the generation process. The core idea is to combine the powerful image
  generation capability of the 2D diffusion model and the texture alignment
  ability of the repainting strategy for generating high-quality multi-view
  images with consistency. We further propose visibility-aware adaptive
  repainting strength for overlap regions to enhance the generated image quality
  in the repainting process. The generated high-quality and multi-view consistent
  images enable the use of simple Mean Square Error (MSE) loss for fast 3D
  content generation. We conduct extensive experiments and show that our method
  has a superior ability to generate high-quality 3D content with multi-view
  consistency and fine textures in 2 minutes from scratch. Code is at
  https://github.com/junwuzhang19/repaint123.
paper_authors._id: 6583b6e33a84a401859e4a92
paper_authors.name: Junwu Zhang
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paper_authors.fullname: Junwu Zhang
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paper_authors.name: Zhenyu Tang
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paper_authors.fullname: Zhenyu Tang
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paper_authors.user: royjames
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paper_authors.name: Yatian Pang
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paper_authors.name: Xinhua Cheng
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paper_authors.fullname: Xinhua Cheng
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paper_authors.name: Peng Jin
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paper_authors.fullname: Peng Jin
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paper_authors.user: Chat-UniVi
paper_authors._id: 6583b6e33a84a401859e4a97
paper_authors.name: Yida Wei
paper_authors._id: 6583b6e33a84a401859e4a98
paper_authors.name: Wangbo Yu
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paper_authors.fullname: Wangbo Yu
paper_authors.type: user
paper_authors.user: Drexubery
paper_authors._id: 6583b6e33a84a401859e4a99
paper_authors.name: Munan Ning
paper_authors.status: admin_assigned
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paper_authors.fullname: Munan Ning
paper_authors.type: user
paper_authors.user: MasonNing
paper_authors._id: 6583b6e33a84a401859e4a9a
paper_authors.name: Li Yuan
paper_authors.status: claimed_verified
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paper_title: Repaint123: Fast and High-quality One Image to 3D Generation with
    Progressive Controllable 2D Repainting
paper_summary: Recent one image to 3D generation methods commonly adopt Score Distillation
  Sampling (SDS). Despite the impressive results, there are multiple deficiencies
  including multi-view inconsistency, over-saturated and over-smoothed textures,
  as well as the slow generation speed. To address these deficiencies, we present
  Repaint123 to alleviate multi-view bias as well as texture degradation and
  speed up the generation process. The core idea is to combine the powerful image
  generation capability of the 2D diffusion model and the texture alignment
  ability of the repainting strategy for generating high-quality multi-view
  images with consistency. We further propose visibility-aware adaptive
  repainting strength for overlap regions to enhance the generated image quality
  in the repainting process. The generated high-quality and multi-view consistent
  images enable the use of simple Mean Square Error (MSE) loss for fast 3D
  content generation. We conduct extensive experiments and show that our method
  has a superior ability to generate high-quality 3D content with multi-view
  consistency and fine textures in 2 minutes from scratch. Code is at
  https://github.com/junwuzhang19/repaint123.
paper_ai_summary: A method combining 2D diffusion models and repainting strategies improves 3D image generation quality, consistency, and speed.
paper_ai_keywords: Score Distillation Sampling 2D diffusion model repainting strategy visibility-aware adaptive repainting strength multi-view consistent images Mean Square Error (MSE) loss