Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors

TL;DR

Magic123 introduces a two-stage method that generates high-quality, textured 3D meshes from a single image by leveraging both 2D and 3D diffusion priors, improving over previous techniques.

Contribution

It proposes a novel coarse-to-fine approach combining 2D and 3D diffusion priors with a controllable trade-off parameter for better 3D object generation from images.

Findings

Significant improvement over previous image-to-3D methods.

Effective use of a single parameter to balance exploration and exploitation.

Validated on synthetic benchmarks and real-world images.

Abstract

We present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images. Our code, models, and generated 3D assets are available at https://github.com/guochengqian/Magic123.