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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 usingboth2D and 3D priors. In the first stage, we optimize a neural radiance fieldto produce a coarse geometry. In the second stage, we adopt a memory-efficientdifferentiable mesh representation to yield a high-resolution mesh with avisually appealing texture. In both stages, the 3D content is learned throughreference view supervision and novel views guided by a combination of 2D and 3Ddiffusion priors. We introduce a single trade-off parameter between the 2D and3D priors to control exploration (more imaginative) and exploitation (moreprecise) of the generated geometry. Additionally, we employ textual inversionand monocular depth regularization to encourage consistent appearances acrossviews and to prevent degenerate solutions, respectively. Magic123 demonstratesa significant improvement over previous image-to-3D techniques, as validatedthrough extensive experiments on synthetic benchmarks and diverse real-worldimages. Our code, models, and generated 3D assets are available athttps://github.com/guochengqian/Magic123.

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