Generalization over Memorization: Generalization-Aware Diffusion Adaptation for Single-Image Multi-View 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, 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/

Single-image novel view synthesis requires viewpoint-dependent structural changes while retaining source-scene information and inferring unobserved content. We formulate this task as a camera-conditioned initialization problem for a frozen pretrained latent diffusion model. At an intermediate DDIM inversion timestep, we decompose the source representation into a structural mean and a source noise residual. A pose-conditioned translation U-Net (TUNet) learns to predict the target-view mean from the source mean. We then introduce a score-guided fusion strategy: the predicted mean and source noise residual form a proposal that is evaluated by the frozen denoiser, whose noise prediction is combined with the translated mean to initialize DDIM sampling. Only TUNet is trained, while the pretrained model synthesizes the final view without explicit reconstruction or rendering of a shared 3D scene. Experiments on MVImgNet and RealEstate10K demonstrate competitive performance. Beyond image-level metrics, Wasserstein comparisons show that the corrected initialization yields trajectory-curvature distributions closer to those of ground-truth target-initialized sampling than raw fusion. These results support structural translation and source-conditioned initialization as a practical approach to reusing frozen diffusion priors for novel view synthesis.

We present VGGT-Diff, a geometry-routed multi-view diffusion model for sparse-view novel view synthesis. Existing novel view synthesis (NVS) methods face a fundamental trade-off: reconstruction-based approaches preserve observed geometry but struggle to synthesize unseen regions, while diffusion-based methods provide strong generative priors yet rely on implicit source-to-query correspondence. VGGT-Diff bridges these regimes by routing visual geometry latents from VGGT-Ω into a pretrained video diffusion model. Each visual token is associated with a 3D point and confidence, then transformed into query-aligned latent conditions through a confidence-aware Visual Geometry Router (VGR) that preserves front and back surface evidence. These conditions guide joint target-view denoising, while Point-Track Residual Consistency (PTRC) regularizes predicted-clean residuals along reliable 3D tracks, improving multi-view stability. We further introduce robust geometry conditioning, combining training-time regularization with inference-time guidance for improved robustness. Experiments show competitive or state-of-the-art performance across interpolation and extrapolation under different viewpoint difficulties. Our code is available at https://github.com/chenkangjie1123/VGGT-Diff.