MuVieCAST: Multi-View Consistent Artistic Style Transfer
This addresses the challenge of maintaining style coherence in multi-view applications like novel-view synthesis for computer graphics and vision, though it is incremental as it builds on existing style transfer techniques.
The paper tackles the problem of achieving consistent artistic style transfer across multiple viewpoints of a scene, and the result is a modular network architecture that generates stylized images with high consistency, preferred by users in 68% of cases compared to state-of-the-art methods.
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