  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.
