让动态3D场景同时实现外观与几何风格迁移，效果更真实连贯。
GAS-NeRF: Geometry-Aware Stylization of Dynamic Radiance Fields
- 用深度图提取风格图像的几何特征，融合到动态辐射场中
- 在合成与真实数据集上显著提升风格化质量与时间一致性
- 适合做动态3D内容创作的艺术家与影视制作人员
当前3D风格化技术主要针对静态场景，而现实世界充满动态物体与变化环境。现有风格迁移方法多关注外观（如颜色、纹理）转换，却常忽略风格图像的几何特征，导致风格化效果不完整或不一致。为此，我们提出GAS-NeRF，一种面向动态辐射场的联合外观与几何风格化新方法。该方法利用深度图提取并迁移风格图像的几何细节至辐射场，随后进行外观转移。在合成与真实世界数据集上的实验表明，该方法显著提升了动态场景的风格化质量，并保持了良好的时间连贯性。
原文摘要 · Abstract (English)
Current 3D stylization techniques primarily focus on static scenes, while our world is inherently dynamic, filled with moving objects and changing environments. Existing style transfer methods primarily target appearance -- such as color and texture transformation -- but often neglect the geometric characteristics of the style image, which are crucial for achieving a complete and coherent stylization effect. To overcome these shortcomings, we propose GAS-NeRF, a novel approach for joint appearance and geometry stylization in dynamic Radiance Fields. Our method leverages depth maps to extract and transfer geometric details into the radiance field, followed by appearance transfer. Experimental results on synthetic and real-world datasets demonstrate that our approach significantly enhances the stylization quality while maintaining temporal coherence in dynamic scenes.
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