Face editing has slowly become possible because of Generative Adversarial Networks. The extension to video editing, on the other hand, is troublesome. This is because it introduces a new challenge: maintaining temporal coherency.
A new study proposes to meet this difficulty by employing specific latent-editing techniques with a non-temporal StyleGAN model off the shelf. The novel method could be incredible for Video Editing Software Market as it generates significant face modifications maintains a greater temporal consistency. Further, It can edit immensely challenging talking head films with much movement and complicated backgrounds, which is something that current approaches can't do.
The researchers assume that the initial video is consistent and that the editing only needs to keep it that way. The points at which temporal inconsistencies are likely to occur are identified, and strategies to reduce these inconsistencies are offered.
Faces in actual films can be easily modified using the provided editing pipeline and latent-based semantic alterations. It can edit challenging talking head films with much movement and complicated backgrounds. This is something that current approaches can't do. The team believes this barrier is primarily artificial.
The raw video is already temporally consistent, and any variations are partly due to the editing pipeline's sloppy treatment of individual components. Researchers used StyleGAN's inherent alignment and neural networks' proclivity for learning low-frequency functions. Further, they utilized these insights to offer a framework for semantic face editing in videos, which shows significant progress over the existing state-of-the-art.
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