FixAnything: 3D-Consistent Rendering Refinement via Video Generative Priors

Rendering views using 3D scene representations such as Gaus-sian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or evenpoint clouds produces artifacts when input views are sparse or targetviews lie far from the input. Recent work mitigates these artifacts usingdiffusion-based generative priors, but is specialized to individual repre-sentations and require custom architectures or extensive retraining. Wepresent FixAnything, a single model for fixing a wide range of render-ing artifacts. It does so by repurposing a pretrained video generativemodel, leveraging its implicit multi-view priors with only minimal mod-ification and lightweight finetuning. Our key insight is that even noisily-rendered sequences preserve camera motion and coarse scene structure,allowing cleanup to be formulated as video-to-video translation. To con-trol what scene structure should be preserved, we introduce a binarymask denoting the clean pixels, enabling the model to anchor its outputto high-quality inputs (e.g. training views) while refining the rest. To en-courage FixAnything to produce 3D-consistent renderings that supportdownstream reconstruction, we use camera pose accuracy (recovered viastructure-from-motion) as a reward signal for direct preference optimiza-tion (DPO). Across four distinct 3D representations, FixAnything con-sistently improves rendering quality with lightweight finetuning, demon-strating that a single generalist video prior can replace multiple specialistrefinement pipelines. The simplicity of the framework enables immediateadoption of stronger future video models without architectural redesign.