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
(~15min). 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 aligns with the identity of the input images,
thereby 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.
Figure 1. Provided with an input image and its coarse 3D generation, MagicBoost
effectively boosts it to a high-quality 3D asset within 15 minutes.
From left to right, we show the input image, pesudo multi-view images and coarse
3D results from Instant3D, together with the significantly improved results
produced by our method.
