In recent years, there has been significant progress in 2D generative face models fueled by applications such as animation, synthetic data generation, and digital avatars. However, due to the absence of 3D information, these 2D models often struggle to accurately disentangle facial attributes like pose, expression, and illumination, limiting their editing capabilities. To address this limitation, this paper proposes a 3D controllable generative face model to produce high-quality albedo and precise 3D shape leveraging existing 2D generative models. By combining 2D face generative models with semantic face manipulation, this method enables editing of detailed 3D rendered faces. The proposed framework utilizes an alternating descent optimization approach over shape and albedo. Differentiable rendering is used to train high-quality shapes and albedo without 3D supervision. Moreover, this approach outperforms the state-of-the-art (SOTA) methods in the well-known NoW benchmark for shape reconstruction. It also outperforms the SOTA reconstruction models in recovering rendered faces' identities across novel poses by an average of 10%. Additionally, the paper demonstrates direct control of expressions in 3D faces by exploiting latent space leading to text-based editing of 3D faces.

翻译：近年来，受动画、合成数据生成和数字虚拟人等应用的推动，二维生成式人脸模型取得了显著进展。然而，由于缺乏三维信息，这些二维模型常难以准确解耦面部属性（如姿态、表情和光照），从而限制了其编辑能力。为解决这一局限，本文提出了一种三维可控生成式人脸模型，能够利用现有二维生成模型生成高质量反照率和精确的三维形状。通过将二维人脸生成模型与语义面部操控相结合，该方法实现了对精细三维渲染人脸的编辑。所提出的框架采用形状与反照率交替下降优化方法，并利用可微分渲染在无三维监督的情况下训练高质量的形状与反照率。此外，该方法在著名的NoW基准测试中，在形状重建任务上优于当前最优方法，并在恢复不同姿态下渲染人脸身份信息方面，平均性能较最优重建模型提升10%。同时，本文展示了通过利用潜在空间直接控制三维人脸表情的能力，从而实现了基于文本的三维人脸编辑。