  Transfer learning of large-scale Text-to-Image (T2I) models has recently
shown impressive potential for Novel View Synthesis (NVS) of diverse objects
from a single image. While previous methods typically train large models on
multi-view datasets for NVS, fine-tuning the whole parameters of T2I models not
only demands a high cost but also reduces the generalization capacity of T2I
models in generating diverse images in a new domain. In this study, we propose
an effective method, dubbed NVS-Adapter, which is a plug-and-play module for a
T2I model, to synthesize novel multi-views of visual objects while fully
exploiting the generalization capacity of T2I models. NVS-Adapter consists of
two main components; view-consistency cross-attention learns the visual
correspondences to align the local details of view features, and global
semantic conditioning aligns the semantic structure of generated views with the
reference view. Experimental results demonstrate that the NVS-Adapter can
effectively synthesize geometrically consistent multi-views and also achieve
high performance on benchmarks without full fine-tuning of T2I models. The code
and data are publicly available in
~\href{https://postech-cvlab.github.io/nvsadapter/}{https://postech-cvlab.github.io/nvsadapter/}.
