仅用一张图生成可实时驱动的高保真3D人脸模型
Generalizable and Animatable Gaussian Head Avatar
- 单次前向传播生成3D高斯参数，无需神经辐射场
- 双提升机制实现身份与表情细节精准还原
- 支持未见身份重建，实时渲染适合交互应用
本文提出一种通用且可动画化的3D高斯人脸头像生成方法（GAGAvatar），仅需单张图像即可完成可动画头像重建。现有方法依赖神经辐射场，存在渲染开销大、重演速度慢的问题。我们通过单次前向传播生成3D高斯参数，关键创新在于提出的双提升方法，能够生成高保真3D高斯点云，精确捕捉身份特征与面部细节。同时，利用全局图像特征和3D形态模型构建用于表情控制的3D高斯表示。训练完成后，模型可在不进行特定优化的情况下重建未知身份，并实现实时重演渲染。实验表明，本方法在重建质量和表情准确性方面优于现有方法。代码与演示已公开于https://github.com/xg-chu/GAGAvatar。
原文摘要 · Abstract (English)
In this paper, we propose Generalizable and Animatable Gaussian head Avatar (GAGAvatar) for one-shot animatable head avatar reconstruction. Existing methods rely on neural radiance fields, leading to heavy rendering consumption and low reenactment speeds. To address these limitations, we generate the parameters of 3D Gaussians from a single image in a single forward pass. The key innovation of our work is the proposed dual-lifting method, which produces high-fidelity 3D Gaussians that capture identity and facial details. Additionally, we leverage global image features and the 3D morphable model to construct 3D Gaussians for controlling expressions. After training, our model can reconstruct unseen identities without specific optimizations and perform reenactment rendering at real-time speeds. Experiments show that our method exhibits superior performance compared to previous methods in terms of reconstruction quality and expression accuracy. We believe our method can establish new benchmarks for future research and advance applications of digital avatars. Code and demos are available https://github.com/xg-chu/GAGAvatar.
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