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.
在本文中，我们提出了一种用于单次可动画头像重建的可泛化和可动画的高斯头像模型（GAGAvatar）。现有方法依赖于神经辐射场，导致渲染开销大且重演速度慢。为了解决这些问题，我们通过单次前向传递从单张图像生成3D高斯参数。我们工作的关键创新在于提出了双提升方法，该方法生成了高保真3D高斯，能够捕捉身份和面部细节。此外，我们利用全局图像特征和3D可变形模型来构建3D高斯以控制表情。在训练完成后，我们的模型可以在不进行特定优化的情况下重建未知身份，并以实时速度进行重演渲染。实验表明，我们的方法在重建质量和表情准确性方面相较于之前的方法表现更优。我们相信，所提出的方法可以为未来的研究树立新的基准，并推动数字化身的应用进展。