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

MuVieCAST 是一种模块化的多视角一致风格转换网络结构，能够在同一场景的多个视角之间实现一致的风格转换，并且支持稀疏和密集视图，适用于各种多视图图像数据集。该方法包括三个模块，分别执行与风格转换相关的特定任务，即内容保留、图像转换和多视角一致性执行。我们通过多个应用领域的实验评估了我们的方法，包括深度图点云融合、网格重建和新视角合成。实验证明，所提出的框架在生成经过风格化的图像方面表现出色，在不同视角之间的结果一致。针对新视角合成的用户研究进一步证实了这些结果，约 68% 的参与者表示与最近的最先进方法相比，更喜欢我们生成的输出。我们的模块化框架可扩展，并且可以轻松集成到各种骨干架构中，是多视图风格转换的灵活解决方案。更多结果请参考我们的项目页面：muviecast.github.io。