Feature axes orthogonalization in semantic face editing

Human image synthesis is the technology that allows a computer program to create realistic photos of non-existing people. Though it is a relatively novel research topic that is mostly used to synthesize human faces, generating moving human figures is also possible using this method.At first, composing the believable and realistic images was a complex process. To achieve decent results, photo-realistic modelling, animating and mapping of the soft dynamics of the human body was required. Nowadays these methods are replaced by approaches based on machine learning and neural networks.Our system is able to create realistic images, consisting of three main components. The first component is a Generative Adversarial Network (GAN) that can generate a random face from a noise vector. Secondly, a convolutional neural network is responsible to recognize facial features on the input photos. Lastly, a regression model computes the correspondence between the input noise vector and output features of the generated face.Using a well-known face dataset, we report results applying the newly proposed model and we also analyze the accuracy and the plausibility of these results.