Animating human face images aims to synthesize a desired source identity in a natural-looking way mimicking a driving video's facial movements. In this context, Generative Adversarial Networks have demonstrated remarkable potential in real-time face reenactment using a single source image, yet are constrained by limited geometry consistency compared to graphic-based approaches. In this paper, we introduce Geometry-guided GAN for Face Animation (G3FA) to tackle this limitation. Our novel approach empowers the face animation model to incorporate 3D information using only 2D images, improving the image generation capabilities of the talking head synthesis model. We integrate inverse rendering techniques to extract 3D facial geometry properties, improving the feedback loop to the generator through a weighted average ensemble of discriminators. In our face reenactment model, we leverage 2D motion warping to capture motion dynamics along with orthogonal ray sampling and volume rendering techniques to produce the ultimate visual output. To evaluate the performance of our G3FA, we conducted comprehensive experiments using various evaluation protocols on VoxCeleb2 and TalkingHead benchmarks to demonstrate the effectiveness of our proposed framework compared to the state-of-the-art real-time face animation methods.

本研究解决了在单一源图像基础上进行自然人脸动画时几何一致性不足的问题。我们提出的几何指导生成对抗网络（G3FA）通过只使用二维图像引入三维信息，显著提升了人脸动画模型的图像生成能力。实验结果表明，G3FA相比现有的实时人脸动画方法在性能上具有显著优势。

G3FA：几何指导的生成对抗网络用于人脸动画