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Found 3 papers, 1 papers with code
One-Shot Face Video Re-enactment using Hybrid Latent Spaces of StyleGAN2
While recent research has progressively overcome the low-resolution constraint of one-shot face video re-enactment with the help of StyleGAN's high-fidelity portrait generation, these approaches rely on at least one of the following: explicit 2D/3D priors, optical flow based warping as motion descriptors, off-the-shelf encoders, etc., which constrain their performance (e. g., inconsistent predictions, inability to capture fine facial details and accessories, poor generalization, artifacts).
Robust One-Shot Face Video Re-enactment using Hybrid Latent Spaces of StyleGAN2
Addressing these limitations, we propose a novel framework exploiting the implicit 3D prior and inherent latent properties of StyleGAN2 to facilitate one-shot face re-enactment at 1024x1024 (1) with zero dependencies on explicit structural priors, (2) accommodating attribute edits, and (3) robust to diverse facial expressions and head poses of the source frame.
Expressive Talking Head Video Encoding in StyleGAN2 Latent-Space
To this end, we propose an end-to-end expressive face video encoding approach that facilitates data-efficient high-quality video re-synthesis by optimizing low-dimensional edits of a single Identity-latent.
