Towards Secure and Usable 3D Assets: A Novel Framework for Automatic Visible Watermarking (WACV 2025 Oral)

📄 Paper: arXiv 2409.00314
🖥️ Conference: WACV 2025 (Oral)
📂 Code Repository: GitHub Link
👨‍💻 Authors: Gursimran Singh, Tianxi Hu, Mohammad Akbari, Qiang Tang, Yong Zhang
🏢 Huawei's AI Gallery Notebook: Link
📹 Video Samples: Download

Framework

Download and extract the code

!wget https://vbdai-notebooks.obs.cn-north-4.myhuaweicloud.com/3dwatermark/code.zip 
!unzip -qo code.zip

Setting up environment

• To start with, confirm the base environment uses Python 3.10.12 and Ubuntu 22.04.2 LTS. Note that older Linux distribution might not support all packages required, and gpu-supported versions for packages may differ depending on your gpu setup.

• Create conda environment with:

!conda create -n "3dwatermark" python=3.10.12 ipython
!conda activate 3dwatermark

• Before installing anything else, install the blender python library:

!pip install bpy==4.0.0

- Install `pytorch3d==0.7.6`:  
!pip install --no-index --no-cache-dir pytorch3d --trusted-host dl.fbaipublicfiles.com -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py310_cu121_pyt221/download.html

• Install other required packages:

!conda env update --file conda_env.yml
!conda install --name 3dwatermark --file spec-file.txt

Watermark 3D Models

• GPU is required for efficient implementation and the default device is cuda:0. The following command can be used to verify the number of GPUs available:

!python -c "import torch; num_of_gpus = torch.cuda.device_count(); print(num_of_gpus)"

• To run the watermarking pipeline with the default example models and parameters, simply run the following:

!python main.py

• Run with watermark embossing:

!python main_curvefollow.py