NAMVIS: Next-Scale Autoregressive Multi-View Image Synthesis
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...
| Authors | Ramil Khafizov, Ilya Statsenko, Ruslan Rakhimov et al. |
|---|---|
| Primary category | cs.CV |
| Published | 3 October 2026 |
| Last updated at source | 3 October 2026 |
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