  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 without any scene specific training. Our method is based on a generalizable transformer architecture and is trained 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 with applying 2D style transfer methods on rendered views. Please refer to our project page for video results: https://ava-nvs.github.io/ 
Adjustable Visual Appearance for Generalizable Novel View Synthesis