  We present a simple, modular, and generic method that upsamples coarse 3D
models by adding geometric and appearance details. While generative 3D models
now exist, they do not yet match the quality of their counterparts in image and
video domains. We demonstrate that it is possible to directly repurpose
existing (pretrained) video models for 3D super-resolution and thus sidestep
the problem of the shortage of large repositories of high-quality 3D training
models. We describe how to repurpose video upsampling models, which are not 3D
consistent, and combine them with 3D consolidation to produce 3D-consistent
results. As output, we produce high quality Gaussian Splat models, which are
object centric and effective. Our method is category agnostic and can be easily
incorporated into existing 3D workflows. We evaluate our proposed SuperGaussian
on a variety of 3D inputs, which are diverse both in terms of complexity and
representation (e.g., Gaussian Splats or NeRFs), and demonstrate that our
simple method significantly improves the fidelity of the final 3D models. Check
our project website for details: supergaussian.github.io
