Magic-Boost: Boost 3D Generation with Mutli-View Conditioned Diffusion
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/)
6615ed2993263a2d79d0c04b
Fan Yang
6615ed2993263a2d79d0c04c
Jianfeng Zhang
6615ed2993263a2d79d0c04d
Yichun Shi
6615ed2993263a2d79d0c04e
Bowen Chen
claimed_verified
2025-06-30T06:24:17.001Z
6361dd166945df7441b893fa
/avatars/b3ae6888a41aab8c2a7ef9f7320565c4.svg
Bowen Chen 
user
chenbowen
6615ed2993263a2d79d0c04f
Chenxu Zhang
6615ed2993263a2d79d0c050
Huichao Zhang
6615ed2993263a2d79d0c051
Xiaofeng Yang
6615ed2993263a2d79d0c052
Jiashi Feng
6615ed2993263a2d79d0c053
Guosheng Lin
Magic-Boost: Boost 3D Generation with Mutli-View Conditioned Diffusion
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/)
Magic-Boost, a multi-view conditioned diffusion model, refines 3D content by enhancing geometry and texture of initial generative results through SDS optimization.
2D diffusion models pre-trained diffusion models multi-view images fast-NeRFs large reconstruction models Magic-Boost multi-view conditioned diffusion model SDS optimization pseudo synthesized multi-view images robust capability SDS guidance high-quality 3D assets rich geometric and textural details