  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/
