NVIDIA's ArtiFixer Beats Prior 3D Scene Reconstruction by 3 dB

3D Gaussian Splatting (3DGS) is one of the most powerful tools available for real-time scene reconstruction and novel view synthesis. But it has a well-known blind spot: it only knows what the camera saw. Fly to an angle the camera never visited, and the reconstruction collapses into floaters, holes, and visual noise. NVIDIA Research's ArtiFixer is a new open model that directly targets this failure mode, and the results are striking.

The problem no one had cleanly solved

Per-scene optimization methods such as 3D Gaussian Splatting provide state-of-the-art novel view synthesis quality but extrapolate poorly to under-observed areas. Prior work tried to fix this with generative models, but ran into two hard walls. Methods that leverage generative priors to correct artifacts hold promise but currently suffer from two shortcomings: scalability, as existing methods use image diffusion models or bidirectional video models that are limited in the number of views they can generate in a single pass, requiring a costly iterative distillation process for consistency. The second problem was quality: generators trained on corrupted renderings tend to freeze up in completely unobserved regions where every input pixel is black, a failure mode called mode collapse.

The core tension is between consistency (respecting what the camera did capture) and generative capacity (hallucinating plausible content where it didn't). Previous methods could do one or the other, but not both at the same time.

Two clever ideas, stacked

ArtiFixer solves this with a two-stage pipeline built on top of Wan 2.1 T2V-14B, a 14-billion-parameter open video diffusion model. The two key innovations are:

• Opacity mixing: Instead of starting generation from pure Gaussian noise (which loses scene context) or from the degraded rendering directly (which collapses in empty regions), ArtiFixer blends the two based on the rendered opacity map. Where the scene has geometry, it starts from the existing rendering. Where there's nothing, it starts from noise. This gives the model scene fidelity where data exists and full generative freedom where it doesn't.