3D Gaussian Splatting (3DGS) has recently created impressive 3D assets for various applications. However, the copyright of these assets is not well protected as existing watermarking methods are not suited for the 3DGS rendering pipeline considering security, capacity, and invisibility. Besides, these methods often require hours or even days for optimization, limiting the application scenarios. In this paper, we propose GuardSplat, an innovative and efficient framework that effectively protects the copyright of 3DGS assets. Specifically, 1) We first propose a CLIP-guided Message Decoupling Optimization module for training the message decoder, leveraging CLIP's aligning capability and rich representations to achieve a high extraction accuracy with minimal optimization costs, presenting exceptional capacity and efficiency. 2) Then, we propose a Spherical-harmonic-aware (SH-aware) Message Embedding module tailored for 3DGS, which employs a set of SH offsets to seamlessly embed the message into the SH features of each 3D Gaussian while maintaining the original 3D structure. It enables the 3DGS assets to be watermarked with minimal fidelity trade-offs and also prevents malicious users from removing the messages from the model files, meeting the demands for invisibility and security. 3) We further propose an Anti-distortion Message Extraction module to improve robustness against various visual distortions. Extensive experiments demonstrate that GuardSplat outperforms state-of-the-art and achieves fast optimization speed. Project page: https://narcissusex.github.io/GuardSplat, and Code: https://github.com/NarcissusEx/GuardSplat.

翻译：3D高斯泼溅（3DGS）技术近期为各类应用创造了令人印象深刻的3D数字资产。然而，由于现有水印方法在安全性、容量和隐蔽性方面难以适配3DGS渲染管线，这些资产的版权保护仍存在不足。此外，现有方法常需数小时甚至数天的优化时间，限制了实际应用场景。本文提出GuardSplat——一种创新高效的保护3DGS资产版权的框架。具体而言：1）我们首先提出CLIP引导的消息解耦优化模块，通过利用CLIP的对齐能力和丰富表征，以最小优化成本实现高提取精度，展现出卓越的容量与效率；2）继而设计专为3DGS定制的球谐感知消息嵌入模块，通过一组球谐偏移量将消息无缝嵌入每个3D高斯的球谐特征中，在保持原始3D结构的同时，使3DGS资产能以最小视觉保真度损失完成水印嵌入，并有效防止恶意用户从模型文件中移除水印，满足隐蔽性与安全性需求；3）进一步提出抗失真消息提取模块，提升针对各类视觉失真的鲁棒性。大量实验表明，GuardSplat在多项指标上超越现有最优方法，并具备快速优化能力。项目主页：https://narcissusex.github.io/GuardSplat，代码仓库：https://github.com/NarcissusEx/GuardSplat。