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

翻译：我们提出了一种简单、模块化且通用的方法，通过添加几何与外观细节来上采样粗糙的三维模型。尽管生成式三维模型现已存在，但其质量尚未达到图像和视频领域同类模型的水平。我们证明，可以直接将现有的（预训练）视频模型重新用于三维超分辨率任务，从而规避高质量三维训练数据大规模匮乏的问题。我们阐述了如何重新利用不具备三维一致性的视频上采样模型，并将其与三维整合技术相结合，以生成具有三维一致性的结果。作为输出，我们生成以对象为中心且高效的高质量高斯泼溅模型。我们的方法具有类别无关性，可轻松集成到现有的三维工作流程中。我们在多种三维输入上评估了所提出的SuperGaussian方法，这些输入在复杂度和表示形式（例如高斯泼溅或神经辐射场）方面均具有多样性，结果表明我们的简单方法显著提升了最终三维模型的保真度。详情请访问我们的项目网站：supergaussian.github.io