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 entirely in completely unobserved regions. To solve these, we propose a two-stage pipeline that leverages two key insights. First, we train a powerful bidirectional generative model with a novel opacity mixing strategy that encourages consistency with existing observations while retaining the model's ability to extrapolate novel content in unseen areas. Second, we distill it into a causal auto-regressive model that generates hundreds of frames in a single pass. This model can directly produce novel views or serve as pseudo-supervision to improve the underlying 3D representation in a simple and highly efficient manner. We evaluate our method extensively and demonstrate that it can generate plausible reconstructions in scenarios where existing approaches fail completely. When measured on commonly benchmarked datasets, we outperform all existing baselines by a wide margin, exceeding prior state-of-the-art methods by 1-3 dB PSNR.

翻译：逐场景优化方法（如三维高斯泼溅）在新视角合成质量上达到顶尖水平，但在欠观测区域的泛化能力较差。利用生成先验修正这些区域伪影的方法虽具潜力，但当前存在两个缺陷：其一为可扩展性，现有方法使用图像扩散模型或双向视频模型，单次能生成的视角数量有限（因此需要昂贵的迭代蒸馏过程来保证一致性）；其二是生成质量本身，先前工作中的生成器往往输出与现有场景内容不一致的结果，并在完全未观测区域彻底失效。为解决这些问题，我们提出两阶段流水线，其核心基于两个关键洞察：首先，我们训练了一个强大的双向生成模型，通过新颖的不透明度混合策略，在鼓励与现有观测一致性的同时，保留模型在未见区域外推新内容的能力。其次，我们将其蒸馏为因果自回归模型，该模型能单次生成数百帧。该模型可直接生成新视角，或作为伪监督以简单高效的方式改进底层三维表示。我们通过广泛评估证明，本方法能在现有方法完全失效的情景下生成合理的重建结果。在常用基准数据集上，我们以显著优势超越所有现有基线方法，PSNR指标较先前最佳方法高出1-3 dB。