Neural Radiance Fields (NeRF) have emerged as a powerful tool for creating highly detailed and photorealistic scenes. Existing methods for NeRF-based 3D style transfer need extensive per-scene optimization for single or multiple styles, limiting the applicability and efficiency of 3D style transfer. In this work, we overcome the limitations of existing methods by rendering stylized novel views from a NeRF without the need for per-scene or per-style optimization. To this end, we take advantage of a generalizable NeRF model to facilitate style transfer in 3D, thereby enabling the use of a single learned model across various scenes. By incorporating a hypernetwork into a generalizable NeRF, our approach enables on-the-fly generation of stylized novel views. Moreover, we introduce a novel flow-based multi-view consistency loss to preserve consistency across multiple views. We evaluate our method across various scenes and artistic styles and show its performance in generating high-quality and multi-view consistent stylized images without the need for a scene-specific implicit model. Our findings demonstrate that this approach not only achieves a good visual quality comparable to that of per-scene methods but also significantly enhances efficiency and applicability, marking a notable advancement in the field of 3D style transfer.

本研究解决了现有神经辐射场（NeRF）在3D风格迁移中需进行每场景优化的问题，提升了其适用性和效率。通过引入可泛化的NeRF模型与超网络，我们实现了在不同场景中无需场景特定模型就能生成风格化新视图的能力。研究表明，该方法在视觉质量上与逐场景方法相当，同时显著提高了效率和适用性，标志着3D风格迁移领域的一大进展。

G3DST：通过神经辐射场在场景和风格间推广3D风格迁移