Abstract
As the application of neural radiance fields (NeRFs) in various 3D vision tasks continues to expand, numerous NeRF-based style transfer techniques have been developed. However, existing methods typically integrate style statistics into the original NeRF pipeline, often leading to suboptimal results in both content preservation and artistic stylization. In this paper, we present multi-level dynamic style transfer for NeRFs (MDS-NeRF), a novel approach that reengineers the NeRF pipeline specifically for stylization and incorporates an innovative dynamic style injection module. Particularly, we propose a multi-level feature adaptor that helps generate a multi-level feature grid representation from the content radiance field, effectively capturing the multi-scale spatial structure of the scene. In addition, we present a dynamic style injection module that learns to extract relevant style features and adaptively integrates them into the content patterns. The stylized multi-level features are then transformed into the final stylized view through our proposed multi-level cascade decoder. Furthermore, we extend our 3D style transfer method to support omni-view style transfer using 3D style references. Extensive experiments demonstrate that MDS-NeRF achieves outstanding performance for 3D style transfer, preserving multi-scale spatial structures while effectively transferring stylistic characteristics.
Abstract (translated)
随着神经辐射场（NeRFs）在各种三维视觉任务中的应用不断扩展，已经开发出了多种基于NeRF的风格转换技术。然而，现有方法通常将风格统计信息整合到原始的NeRF流程中，这往往导致内容保留和艺术风格化方面的结果不尽如人意。本文介绍了一种名为多级动态风格传输（MDS-NeRF）的新方法，该方法专门重新设计了用于风格化的NeRF管道，并引入了一个创新性的动态样式注入模块。 具体而言，我们提出了一个多级特征适配器，它有助于从内容辐射场中生成一个多级特征网格表示，有效捕捉场景的多尺度空间结构。此外，我们还介绍了一个动态样式注入模块，该模块能够学习提取相关风格特征，并将它们自适应地集成到内容模式中。通过我们的提出的多级级联解码器，这些经过风格化的多级特性随后被转换为最终的风格化视图。 另外，我们将3D风格传输方法扩展以支持使用3D样式参考进行全方位风格传输。大量的实验表明，MDS-NeRF在保持多尺度空间结构的同时能够有效地转移艺术特征，在三维风格传输方面取得了卓越的表现。
URL
https://arxiv.org/abs/2510.00592
