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.

Magic123提出通过使用2D和3D先验知识，用单张无姿态图像实现高质量纹理3D网格的生成，通过参考视图监督和新视角的结合引导学习3D内容，并在训练过程中引入单一权衡参数控制对生成几何结构的探索和开发。此外，该模型采用文本反演和单目深度正则化以保证视角一致性和防止退化解，并在综合合成和真实场景测试中体现了较大提升。

Magic123: 使用二维和三维扩散先验进行单图像高质量三维物体生成