We present a generalizable novel view synthesis method where it is possible to modify the visual appearance of rendered views to match a target weather or lighting condition. Our method is based on a generalizable transformer architecture, trained on synthetically generated scenes under different appearance conditions. This allows for rendering novel views in a consistent manner of 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 both real and synthetic scenes are provided including both qualitative and quantitative evaluations. Please refer to our project page for video results: https://ava-nvs.github.io/

翻译：我们提出了一种可泛化的新视角合成方法，能够调整渲染视图的视觉表现以匹配目标天气或光照条件。该方法基于可泛化的Transformer架构，并使用在不同表现条件下生成的合成场景进行训练。这使得我们能够以一致的方式渲染训练集中未包含的三维场景的新视角，同时具备以下能力：（i）调整其视觉表现以匹配目标条件；（ii）在不同条件之间进行平滑插值。我们提供了在真实场景与合成场景上的实验，包括定性评估与定量分析。视频结果详见项目页面：https://ava-nvs.github.io/