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

翻译：我们提出了MuVieCAST，一种模块化的多视角一致风格迁移网络架构，能够实现同一场景多个视角间一致的风格迁移。该网络架构支持稀疏视图和密集视图，使其能够灵活处理多种多视角图像数据集。该方法由三个模块组成，分别执行风格迁移相关的特定任务，即内容保持、图像变换和多视角一致性增强。我们在多个应用领域（包括基于深度图的点云融合、网格重建和新视角合成）中广泛评估了该方法。实验表明，所提出的框架能够生成高质量的风格化图像，并在不同视角下呈现一致的结果。一项专注于新视角合成的用户研究进一步证实了这些结果，约68%的案例中，参与者更偏好我们生成的输出，而非近期最先进的方法。我们的模块化框架具有可扩展性，能轻松集成各种骨干架构，为多视角风格迁移提供了灵活解决方案。更多结果见项目页面：muviecast.github.io