6 papers with code • 0 benchmarks • 0 datasets
These leaderboards are used to track progress in Facial Editing
Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive.
The key idea is pivotal tuning - a brief training process that preserves the editing quality of an in-domain latent region, while changing its portrayed identity and appearance.
In this work, we propose Talk-to-Edit, an interactive facial editing framework that performs fine-grained attribute manipulation through dialog between the user and the system.
The ability of Generative Adversarial Networks to encode rich semantics within their latent space has been widely adopted for facial image editing.
Our framework elevates the resolution of the synthesized talking face to 1024*1024 for the first time, even though the training dataset has a lower resolution.
In this study, we highlight the importance of interaction in a dual-space GAN for more controllable editing.