MuVieCAST: Multi-View Consistent Artistic Style Transfer

Nail Ibrahimli, Julian Kooij, Liangliang Nan

We present a modular multi-view consistent style transfer network architecture MuVieCAST 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 evaluate our approach extensively across multiple application domains including depth-map-based point cloud fusion, mesh reconstruction, and novel-view synthesis. The results demonstrate that the framework produces high-quality stylized images while maintaining consistency across multiple views, even for complex styles that involve mosaic tessellations or extensive brush strokes. Our modular multi-view consistent style transfer framework is extensible and can easily be integrated with various backbone architectures, making it a flexible solution for multi-view style transfer. Project page: https://muviecast.github.io/