通过GAN潜在空间中的方向发现实现单样本神经人脸重演
One-shot Neural Face Reenactment via Finding Directions in GAN's Latent Space
AI总结：
提出一种利用预训练GAN潜在空间方向发现的方法，实现单样本神经人脸重演，通过3D形状模型学习控制姿态和表情的方向，在VoxCeleb基准上优于现有方法。
摘要：
在本文中，我们提出了用于神经人脸/头部重演的框架，其目标是将目标人脸的3D头部朝向和表情迁移到源人脸。以往的方法侧重于学习用于身份和头部姿态/表情解耦的嵌入网络，这被证明是一项相当困难的任务，会降低生成图像的质量。我们采取了一种不同的方法，绕过了此类网络的训练，通过使用（微调的）预训练GAN，这些GAN已被证明能够生成高质量的人脸图像。由于GAN具有弱可控性的特点，我们方法的核心是一种发现GAN潜在空间中哪些方向负责控制头部姿态和表情变化的方法。我们提出了一个简单的流程，借助3D形状模型来学习这些方向，该模型在构造上固有地捕获了头部姿态、身份和表情的解耦方向。此外，我们表明，通过将真实图像嵌入GAN潜在空间，我们的方法可以成功地用于真实世界人脸的重演。我们的方法具有若干有利特性，包括使用单一源图像（单样本）以及支持跨人物重演。大量的定性和定量结果表明，在VoxCeleb1和VoxCeleb2的标准基准上，我们的方法通常产生比最先进方法显著更高质量的重演人脸。
英文摘要：
In this paper, we present our framework for neural face/head reenactment whose goal is to transfer the 3D head orientation and expression of a target face to a source face. Previous methods focus on learning embedding networks for identity and head pose/expression disentanglement which proves to be a rather hard task, degrading the quality of the generated images. We take a different approach, bypassing the training of such networks, by using (fine-tuned) pre-trained GANs which have been shown capable of producing high-quality facial images. Because GANs are characterized by weak controllability, the core of our approach is a method to discover which directions in latent GAN space are responsible for controlling head pose and expression variations. We present a simple pipeline to learn such directions with the aid of a 3D shape model which, by construction, inherently captures disentangled directions for head pose, identity, and expression. Moreover, we show that by embedding real images in the GAN latent space, our method can be successfully used for the reenactment of real-world faces. Our method features several favorable properties including using a single source image (one-shot) and enabling cross-person reenactment. Extensive qualitative and quantitative results show that our approach typically produces reenacted faces of notably higher quality than those produced by state-of-the-art methods for the standard benchmarks of VoxCeleb1 & 2.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
