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.
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Current developments in computer vision and deep learning allow to automatically generate hyper-realistic images, hardly distinguishable from real ones.
Instead of simply using a multi-task learning approach to simultaneously detect manipulated images and predict the manipulated mask (regions), we propose to utilize the attention mechanism to process and improve the feature maps of the classifier model.
To be more specific, the encoder-decoder structured generator is used to learn a pose disentangled face representation, and the encoder-decoder structured discriminator is tasked to perform real/fake classification, face reconstruction, determining identity and estimating face pose.
Chinese meme-face is a special kind of internet subculture widely spread in Chinese Social Community Networks.
In this paper, we are interested in generating an cartoon face of a person by using unpaired training data between real faces and cartoon ones.
Recent years have seen fast development in synthesizing realistic human faces using AI technologies.
Portrait editing is a popular subject in photo manipulation. The Generative Adversarial Network (GAN) advances the generating of realistic faces and allows more face editing.
Portrait editing is a popular subject in photo manipulation.
Moreover, we also conduct experiments on a near-infrared dataset containing facial expression videos of drivers to assess the performance using in-the-wild data for driver emotion recognition.