Current 3D GAN inversion methods for human heads typically use only one
single frontal image to reconstruct the whole 3D head model. This leaves out
meaningful information when multi-view data or dynamic videos are available.
Our method builds on existing state-of-the-art 3D GAN inversion techniques to
allow for consistent and simultaneous inversion of multiple views of the same
subject. We employ a multi-latent extension to handle inconsistencies present
in dynamic face videos to re-synthesize consistent 3D representations from the
sequence. As our method uses additional information about the target subject,
we observe significant enhancements in both geometric accuracy and image
quality, particularly when rendering from wide viewing angles. Moreover, we
demonstrate the editability of our inverted 3D renderings, which distinguishes
them from NeRF-based scene reconstructions.

当前针对人头的 3D GAN 反演方法通常只使用一个正面图像来重建整个 3D 头部模型，从而忽略了多视角数据或动态视频时的有意义信息。本研究基于现有先进的 3D GAN 反演技术，允许一致且同时地反演同一主体的多个视角。我们采用多潜变量扩展来处理动态人脸视频中的不一致性，从而从序列中重新合成一致的 3D 表示。由于我们的方法使用了有关目标主体的额外信息，我们观察到在几何精度和图像质量方面显著提高，尤其是在宽视角下的渲染。此外，我们展示了反演 3D 渲染的可编辑性，这使它们与基于 NeRF 的场景重建有所区别。