We present InstantMesh, a feed-forward framework for instant 3D mesh
generation from a single image, featuring state-of-the-art generation quality
and significant training scalability. By synergizing the strengths of an
off-the-shelf multiview diffusion model and a sparse-view reconstruction model
based on the LRM architecture, InstantMesh is able to create diverse 3D assets
within 10 seconds. To enhance the training efficiency and exploit more
geometric supervisions, e.g, depths and normals, we integrate a differentiable
iso-surface extraction module into our framework and directly optimize on the
mesh representation. Experimental results on public datasets demonstrate that
InstantMesh significantly outperforms other latest image-to-3D baselines, both
qualitatively and quantitatively. We release all the code, weights, and demo of
InstantMesh, with the intention that it can make substantial contributions to
the community of 3D generative AI and empower both researchers and content
creators.

InstantMesh 是一种从单个图像进行即时 3D 网格生成的前向框架，具有最先进的生成质量和可扩展的训练能力。通过发挥现有多视角扩散模型和基于 LRM 架构的稀疏视图重建模型的优势，InstantMesh 能够在 10 秒内创建多样化的 3D 资源。通过将可微分的等值面提取模块集成到我们的框架中，并直接在网格表示上进行优化，以提高训练效率和利用更多几何监督，例如深度和法线。公共数据集上的实验结果表明，InstantMesh 在定性和定量上明显优于其他最新的图像到 3D 基准。我们发布了 InstantMesh 的所有代码，权重和演示，希望它能为 3D 生成 AI 社区做出巨大贡献，并赋予研究人员和内容创作者更多的力量。