We introduce a 3D-aware diffusion model, ZeroNVS, for single-image novel view synthesis for in-the-wild scenes. While existing methods are designed for single objects with masked backgrounds, we propose new techniques to address challenges introduced by in-the-wild multi-object scenes with complex backgrounds. Specifically, we train a generative prior on a mixture of data sources that capture object-centric, indoor, and outdoor scenes. To address issues from data mixture such as depth-scale ambiguity, we propose a novel camera conditioning parameterization and normalization scheme. Further, we observe that Score Distillation Sampling (SDS) tends to truncate the distribution of complex backgrounds during distillation of 360-degree scenes, and propose "SDS anchoring" to improve the diversity of synthesized novel views. Our model sets a new state-of-the-art result in LPIPS on the DTU dataset in the zero-shot setting, even outperforming methods specifically trained on DTU. We further adapt the challenging Mip-NeRF 360 dataset as a new benchmark for single-image novel view synthesis, and demonstrate strong performance in this setting. Our code and data are at http://kylesargent.github.io/zeronvs/

翻译：我们提出了一种三维感知扩散模型ZeroNVS，用于野外场景的单张图像新视角合成。现有方法主要针对带掩码背景的单一物体设计，而本文提出新技术应对复杂背景下的多物体野外场景挑战。具体而言，我们在混合数据源上训练生成先验，这些数据源涵盖物体中心、室内和室外场景。针对数据混合带来的深度尺度歧义问题，我们提出新型相机参数化条件与归一化方案。此外，观察到分数蒸馏采样在360度场景蒸馏过程中倾向于截断复杂背景的分布，我们提出"SDS锚定"方法以增强合成新视角的多样性。该模型在零样本设置下的DTU数据集LPIPS指标上达到最新最优结果，甚至超越了专门在DTU上训练的模型。我们进一步将具有挑战性的Mip-NeRF 360数据集适配为单张图像新视角合成的新基准，并在此场景下展现了强大性能。代码与数据见http://kylesargent.github.io/zeronvs/