110 papers with code • 0 benchmarks • 4 datasets
Face generation is the task of generating (or interpolating) new faces from an existing dataset.
The state-of-the-art results for this task are located in the Image Generation parent.
These leaderboards are used to track progress in Face Generation
LibrariesUse these libraries to find Face Generation models and implementations
Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability.
We propose a novel attributes encoder for extracting multi-level target face attributes, and a new generator with carefully designed Adaptive Attentional Denormalization (AAD) layers to adaptively integrate the identity and the attributes for face synthesis.
Recent advances in Generative Adversarial Networks (GANs) have shown impressive results for task of facial expression synthesis.
In this work, we propose a novel framework, called InterFaceGAN, for semantic face editing by interpreting the latent semantics learned by GANs.
Speech is a rich biometric signal that contains information about the identity, gender and emotional state of the speaker.