ArtiFixer: Enhancing and Extending 3D Reconstruction with Auto-Regressive Diffusion Models
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. Methods that leverage generative priors to correct artifacts in these areas hold promise but currently suffer from two shortcomings. The first is 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 (and thus require a costly iterative distillation process for consistency). The second is quality itself, as generators used in prior work tend to produce outputs that are inconsistent with existing scene content and fail e...
| Authors | Riccardo de Lutio, Tobias Fischer, Yen-Yu Chang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 February 2026 |
| Last updated at source | 5 May 2026 |
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