As the metaverse rapidly develops, 3D facial age transformation will attract increasing attention, as it can bring many potential benefits to a wide variety of users, e.g., 3D aging figures creation, 3D facial data augmentation and attribute editing. Compared with 2D methods, 3D face aging is an underexplored problem. To fill this gap, we developed a new mesh-to-mesh Wasserstein GAN (MeshWGAN) with a multi-task gradient penalty to model a continuous bi-directional 3D facial geometric aging process.
This code is inspired by 3D mesh network: SpiralNet++, and 2D face aging network LATS. We also used these methods: 2D face aging network SAM, 3D facial details prediction FaceScape, 3D face reconstruction Deep3DFace, 3D mesh registration NICP.
If you find our work useful to your research, please consider citing:
@article{Jie2023Mesh,
title={{MeshWGAN}: Mesh-to-Mesh Wasserstein {GAN} with Multi-Task Gradient Penalty for {3D} Facial Geometric Age Transformation},
author={Jie Zhang and Kangneng Zhou and Yan Luximon and Tong-Yee Lee and Ping Li},
journal={IEEE Transactions on Visualization and Computer Graphics},
year={2023},
pages={1--14}
}
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