Towards Secure and Usable 3D Assets: A Novel Framework for Automatic Visible Watermarking

Gursimran Singh, Tianxi Hu, Mohammad Akbari, Qiang Tang, Yong Zhang; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 721-730

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 here.