PREreview of StyleFaceV: Face Video Generation via Decomposing and Recomposing Pretrained StyleGAN3

Summary

The research presents StyleFaceV as a system which generates realistic face videos through StyleGAN3 pretraining by separating and recombining appearance and motion data within its hidden layers. The authors solve two main problems in face video synthesis through their method which separates facial appearance from pose and temporal dynamics before their controlled recombination. The research creates a time-based system which learns to produce stable motion patterns by uniting still images with video content during training to achieve maximum data usage. The work advances the field by demonstrating that careful manipulation of pretrained generative model latent spaces can produce identity-preserving, temporally coherent, and high-resolution face videos without requiring high-resolution video training data.

Major issues

The main contribution of the paper depends on StyleGAN3 latent space decomposition between appearance and pose elements but it needs to show quantitative methods for disentanglement assessment instead of depending on visual inspection results. The research requires quantitative measurements and ablation tests to assess the degree of disentanglement which will validate its results.

The temporal model receives a general description but readers need more information about its structure and training behavior and performance restrictions to determine its ability to handle different motion types.

The research requires more details about ethical considerations and system protection systems and boundary definitions to address the dangerous uses of realistic face video generation systems.

Minor issues

The pipeline needs improved visualization through a detailed step-by-step diagram which shows the decomposition and recomposition and temporal modeling stages.

The document contains implementation and training information which appears throughout different sections of the document; a better organization of this content would enhance the document's clarity.

The dense technical content in the methodology section needs only basic language adjustments to help readers understand it better.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.