  We introduce MuVieCAST, a modular multi-view consistent style transfer
network architecture that enables consistent style transfer between multiple
viewpoints of the same scene. This network architecture supports both sparse
and dense views, making it versatile enough to handle a wide range of
multi-view image datasets. The approach consists of three modules that perform
specific tasks related to style transfer, namely content preservation, image
transformation, and multi-view consistency enforcement. We extensively evaluate
our approach across multiple application domains including depth-map-based
point cloud fusion, mesh reconstruction, and novel-view synthesis. Our
experiments reveal that the proposed framework achieves an exceptional
generation of stylized images, exhibiting consistent outcomes across
perspectives. A user study focusing on novel-view synthesis further confirms
these results, with approximately 68\% of cases participants expressing a
preference for our generated outputs compared to the recent state-of-the-art
method. Our modular framework is extensible and can easily be integrated with
various backbone architectures, making it a flexible solution for multi-view
style transfer. More results are demonstrated on our project page:
muviecast.github.io
