Multi-view Inversion for 3D-aware Generative Adversarial Networks
Florian Barthel Anna Hilsmann Peter Eisert
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.