FixAnything: 3D-Consistent Rendering Refinement via Video Generative Priors
Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces artifacts when input views are sparse or target views lie far from the input. Recent work mitigates these artifacts using diffusion-based generative priors, but is specialized to individual representations and require custom architectures or extensive retraining. We present FixAnything, a single model for fixing a wide range of rendering artifacts. It does so by repurposing a pretrained video generative model, leveraging its implicit multi-view priors with only minimal modification and lightweight finetuning. Our key insight is that even no...
| Authors | Khiem Vuong, Deva Ramanan, Srinivasa Narasimhan |
|---|---|
| Primary category | cs.CV |
| Published | 24 August 2026 |
| Last updated at source | 24 August 2026 |
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