title: Magic-Boost: Boost 3D Generation with Mutli-View Conditioned Diffusion
summary: Benefiting from the rapid development of 2D diffusion models, 3D content
  creation has made significant progress recently. One promising solution
  involves the fine-tuning of pre-trained 2D diffusion models to harness their
  capacity for producing multi-view images, which are then lifted into accurate
  3D models via methods like fast-NeRFs or large reconstruction models. However,
  as inconsistency still exists and limited generated resolution, the generation
  results of such methods still lack intricate textures and complex geometries.
  To solve this problem, we propose Magic-Boost, a multi-view conditioned
  diffusion model that significantly refines coarse generative results through a
  brief period of SDS optimization (sim15min). Compared to the previous text
  or single image based diffusion models, Magic-Boost exhibits a robust
  capability to generate images with high consistency from pseudo synthesized
  multi-view images. It provides precise SDS guidance that well aligns with the
  identity of the input images, enriching the local detail in both geometry and
  texture of the initial generative results. Extensive experiments show
  Magic-Boost greatly enhances the coarse inputs and generates high-quality 3D
  assets with rich geometric and textural details. (Project Page:
  https://magic-research.github.io/magic-boost/)
paper_authors._id: 6615ed2993263a2d79d0c04b
paper_authors.name: Fan Yang
paper_authors._id: 6615ed2993263a2d79d0c04c
paper_authors.name: Jianfeng Zhang
paper_authors._id: 6615ed2993263a2d79d0c04d
paper_authors.name: Yichun Shi
paper_authors._id: 6615ed2993263a2d79d0c04e
paper_authors.name: Bowen Chen
paper_authors._id: 6615ed2993263a2d79d0c04f
paper_authors.name: Chenxu Zhang
paper_authors._id: 6615ed2993263a2d79d0c050
paper_authors.name: Huichao Zhang
paper_authors._id: 6615ed2993263a2d79d0c051
paper_authors.name: Xiaofeng Yang
paper_authors._id: 6615ed2993263a2d79d0c052
paper_authors.name: Jiashi Feng
paper_authors._id: 6615ed2993263a2d79d0c053
paper_authors.name: Guosheng Lin
paper_title: Magic-Boost: Boost 3D Generation with Mutli-View Conditioned Diffusion
paper_summary: Benefiting from the rapid development of 2D diffusion models, 3D content
  creation has made significant progress recently. One promising solution
  involves the fine-tuning of pre-trained 2D diffusion models to harness their
  capacity for producing multi-view images, which are then lifted into accurate
  3D models via methods like fast-NeRFs or large reconstruction models. However,
  as inconsistency still exists and limited generated resolution, the generation
  results of such methods still lack intricate textures and complex geometries.
  To solve this problem, we propose Magic-Boost, a multi-view conditioned
  diffusion model that significantly refines coarse generative results through a
  brief period of SDS optimization (sim15min). Compared to the previous text
  or single image based diffusion models, Magic-Boost exhibits a robust
  capability to generate images with high consistency from pseudo synthesized
  multi-view images. It provides precise SDS guidance that well aligns with the
  identity of the input images, enriching the local detail in both geometry and
  texture of the initial generative results. Extensive experiments show
  Magic-Boost greatly enhances the coarse inputs and generates high-quality 3D
  assets with rich geometric and textural details. (Project Page:
  https://magic-research.github.io/magic-boost/)
paper_ai_keywords: diffusion models fine-tuning 3D content creation multi-view images fast-NeRFs large reconstruction models Magic-Boost multi-view conditioned diffusion model SDS optimization pseudo synthesized multi-view images high-quality 3D assets