一个NeRF模型可嵌入多个独立水印，不降画质且无需重训练。
MultiNeRF: Multiple Watermark Embedding for Neural Radiance Fields
- 在NeRF中加入专用水印网格，分离内容与水印信号。
- 支持多水印并行嵌入，容量提升且鲁棒性显著增强。
- 适合需批量版权保护的3D内容创作者使用。
我们提出MultiNeRF，一种3D水印技术，可在单个神经辐射场（NeRF）模型渲染的图像中嵌入多个唯一密钥的水印，同时保持高视觉质量。该方法扩展了TensoRF NeRF模型，引入专用水印网格，与原有几何和外观网格并列，实现更高水印容量且避免水印信号与场景内容纠缠。我们设计基于FiLM的条件调制机制，根据输入标识动态激活水印，支持多个独立水印的嵌入与提取，无需模型重新训练。在NeRF-Synthetic和LLFF数据集上验证，水印容量显著提升且渲染质量无损。MultiNeRF将单水印方法推广为灵活的多水印框架，为3D内容版权保护提供可扩展解决方案。
原文摘要 · Abstract (English)
We present MultiNeRF, a 3D watermarking method that embeds multiple uniquely keyed watermarks within images rendered by a single Neural Radiance Field (NeRF) model, whilst maintaining high visual quality. Our approach extends the TensoRF NeRF model by incorporating a dedicated watermark grid alongside the existing geometry and appearance grids. This extension ensures higher watermark capacity without entangling watermark signals with scene content. We propose a FiLM-based conditional modulation mechanism that dynamically activates watermarks based on input identifiers, allowing multiple independent watermarks to be embedded and extracted without requiring model retraining. MultiNeRF is validated on the NeRF-Synthetic and LLFF datasets, with statistically significant improvements in robust capacity without compromising rendering quality. By generalizing single-watermark NeRF methods into a flexible multi-watermarking framework, MultiNeRF provides a scalable solution for 3D content. attribution.
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