NAMVIS: next-scale autoregression beats diffusion for multi-view synthesis, 3x faster

Ramil Khafizov · hf · 2026-10-08

Problem: Sparse-view novel view synthesis is central to 3D content creation, but diffusion approaches are slow due to iterative denoising.
Method: NAMVIS is diffusion-free, recasting multi-view synthesis as geometry-conditioned next-scale autoregression that predicts discrete visual tokens in a few coarse-to-fine scale steps, sampling tokens in parallel within each scale and across views.
Geometry anchoring: Multi-scale Projective Pose Encoding injects source/target camera transforms into self- and cross-attention at every resolution; global conditioning plus dense geometry-aware cross-attention preserves source appearance and target-view consistency.
Results: Outperforms diffusion baselines on PSNR/SSIM/LPIPS across Objaverse, GSO, and OmniObject3D while running over 3x faster.

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