We present a generalizable novel view synthesis method which enables modifying the visual appearance of an observed scene so rendered views match a target weather or lighting condition without any scene specific training or access to reference views at the target condition. Our method is based on a pretrained generalizable transformer architecture and is fine-tuned on synthetically generated scenes under different appearance conditions. This allows for rendering novel views in a consistent manner for 3D scenes that were not included in the training set, along with the ability to (i) modify their appearance to match the target condition and (ii) smoothly interpolate between different conditions. Experiments on real and synthetic scenes show that our method is able to generate 3D consistent renderings while making realistic appearance changes, including qualitative and quantitative comparisons. Please refer to our project page for video results: https://ava-nvs.github.io/

翻译：我们提出了一种通用新视角合成方法，能够在不针对特定场景训练或获取目标条件下参考视图的情况下，调整观察场景的视觉外观，使渲染视图与目标天气或光照条件相匹配。本方法基于预训练的通用Transformer架构，并在不同外观条件下的合成场景上进行微调。这使得我们能够为训练集未包含的三维场景生成一致的新视角渲染，同时具备以下能力：(i) 调整场景外观以匹配目标条件，以及(ii) 在不同条件之间平滑插值。在真实场景与合成场景上的实验表明，本方法在生成三维一致渲染的同时，能实现逼真的外观变化（含定性及定量比较）。视频结果详见项目主页：https://ava-nvs.github.io/