| title | NAMVIS: Next-Scale Autoregressive Multi-View Image Synthesis |
|---|---|
| authors | Ramil Khafizov, Ilya Statsenko, Ruslan Rakhimov, Artem Komarichev, Peter Wonka, Evgeny Burnaev |
| date | 2026-10-03 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.04722 |
| upvotes | 29 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Sparse-view novel view synthesis is a central problem in 3D content creation, but diffusion-based approaches remain limited by iterative denoising, making multi-view generation expensive at inference time. We introduce NAMVIS, a diffusion-free framework that reformulates multi-view image synthesis as geometry-conditioned next-scale autoregression. Instead of generating target views through repeated denoising, NAMVIS predicts discrete visual tokens through a small number of coarse-to-fine scale steps, while sampling all tokens within each scale and across target views in parallel. To anchor this generation process to explicit camera geometry, we propose Multi-scale Projective Pose Encoding, which injects source and target camera transformations into both target-view self-attention and source-to-target cross-attention at every resolution. NAMVIS further combines global conditioning with dense geometry-aware cross-attention, enabling the model to preserve source-view appearance while maintaining target-view consistency. Across Objaverse, GSO, and OmniObject3D, NAMVIS outperforms diffusion-based baselines in PSNR, SSIM, and LPIPS, while running over 3 times faster than the evaluated diffusion baselines under the same evaluation setting. These results suggest that geometry-conditioned next-scale autoregression is a promising and efficient alternative to diffusion for sparse-view multi-view synthesis. Additional qualitative results, videos, and resources are available at https://corl-team.github.io/namvis/ |
cite
@misc{khafizov2026namvis,
title={NAMVIS: Next-Scale Autoregressive Multi-View Image Synthesis},
author={Ramil Khafizov and Ilya Statsenko and Ruslan Rakhimov and Artem Komarichev and Peter Wonka and Evgeny Burnaev},
year={2026},
eprint={2610.04722},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.04722}
}