Modeling large-scale scenes from unconstrained image collections in-the-wild has proven to be a major challenge in computer vision. Existing methods tackling in-the-wild neural rendering operate in a closed-world setting, where knowledge is limited to a scene's captured images within a training set. We propose EvE, which is, to the best of our knowledge, the first method leveraging generative priors to improve in-the-wild scene modeling. We employ pre-trained generative networks to enrich K-Planes representations with extrinsic knowledge. To this end, we define an alternating training procedure to conduct optimization guidance of K-Planes trained on the training set. We carry out extensive experiments and verify the merit of our method on synthetic data as well as real tourism photo collections. EvE enhances rendered scenes with richer details and outperforms the state of the art on the task of novel view synthesis in-the-wild. Our project page can be found at https://eve-nvs.github.io .

翻译：从非受控图像集合中对大规模场景进行建模一直是计算机视觉领域的重大挑战。现有面向非受控环境下的神经渲染方法采用封闭世界设定，其知识仅限于训练集中场景的捕获图像。我们提出EvE——据我们所知，这是首个利用生成式先验提升非受控场景建模能力的方法。通过引入预训练生成网络，我们为K-Planes表示注入外部知识。为此，我们定义了一种交替训练流程，对基于训练集训练的K-Planes进行优化引导。在合成数据与真实旅游照片集上的大量实验验证了本方法的有效性。EvE能够为渲染场景增添更丰富的细节，在非受控环境新视角合成任务中超越现有最优方案。项目页面见https://eve-nvs.github.io。