3D models, particularly AI-generated ones, have witnessed a recent surge across various industries such as entertainment. Hence, there is an alarming need to protect the intellectual property and avoid the misuse of these valuable assets. As a viable solution to address these concerns, we rigorously define the novel task of automated 3D visible watermarking in terms of two competing aspects: watermark quality and asset utility. Moreover, we propose a method of embedding visible watermarks that automatically determines the right location, orientation, and number of watermarks to be placed on arbitrary 3D assets for high watermark quality and asset utility. Our method is based on a novel rigid-body optimization that uses back-propagation to automatically learn transforms for ideal watermark placement. In addition, we propose a novel curvature-matching method for fusing the watermark into the 3D model that further improves readability and security. Finally, we provide a detailed experimental analysis on two benchmark 3D datasets validating the superior performance of our approach in comparison to baselines. Code and demo are available.

本研究针对3D模型，尤其是AI生成模型，的知识产权保护与利用问题，提出了一种自动可见水印的框架。该框架通过新的刚体优化方法和曲率匹配方法，实现水印在3D资产中的最佳位置、方向与数量的自动嵌入，显著提升了水印质量与资产实用性。实验结果显示，该方法在多个基准3D数据集上优于现有方法，具有较高的实用前景。

面向安全可用的3D资产：一种自动可见水印的新框架