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Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors paper page: 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.show more

AK

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305,643 views • 3 years ago •via X (Twitter)

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4 Comments

Gordon Guocheng Qian3 years ago

@_akhaliq Thanks for sharing our work. For people interested in Magic123, here are the useful links: arxiv: website: Code: Code will be released in one month.

Abdullah Hamdi3 years ago

Thanks @_akhaliq for sharing our work

Naina Chaturvedi2 years ago

Good Read. Will be covering it here -

FORTAS Mohammed Tahar2 years ago

From Beginner to Pro: Mastering Random Forest with applications

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14,041 views • 1 month ago

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25,651 views • 1 year ago

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39,338 views • 1 year ago

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194,356 views • 2 years ago