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Researchers have also proposed an in-domain GAN inversion approach to enable the editing of GAN-generated images, allowing for de-aging or the addition of new facial expressions to existing photographs 7. However, StyleGAN is an example of a modifiable GAN that enables intuitive control of the facial details of generated images by separating high-level attributes like the identity of a person from low-level features such as hair or freckles, with few visible artefacts 4. GAN architectures can generate images of things that have never existed before, such as human faces 3, 4. The two networks compete and try to outperform each other in a closed-feedback loop, resulting in a gradual increase of the realism of the generated output.
#Natural voices download for ms agent characters generator
The generator is responsible for generating new content that resembles the input data, while the discriminator’s job is to differentiate the generated or fake output from the real data. The GAN architecture includes two neural networks, a generator and a discriminator. Since their introduction, models for AI-generated media, such as GANs, have enabled the hyper-realistic synthesis of digital content, including the generation of photorealistic images, cloning of voices, animation of faces and translation of images from one form to another 3, 4, 5, 6. Recent leaps in generative models include generative adversarial networks (GANs) 2. Although discriminative models can identify a person in an image, generative models can produce a new image of a person that has never existed before. Generative and discriminative models are two different approaches to machines learning from data. It has taken decades and major leaps in artificial intelligence (AI) for generated content to reach a high level of realism. Unlike today’s synthesized media, computer-generated content from the early era was far from realistic and easily distinguishable from that created by humans. Some of the earliest attempts were focused on replicating human creativity by having computers generate visual art and music 1.
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The idea of computers generating content has been around since the 1950s. As we look towards the future, we foresee generative media as a crucial part of the ever growing landscape of human–AI interaction. We demonstrate an easy-to-use AI character generation pipeline to enable such outcomes and discuss ethical implications as well as the need for including traceability to help maintain trust in the generated media. Although negative use cases of this technology have dominated the conversation so far, in this Perspective we highlight emerging positive use cases of AI-generated characters, specifically in supporting learning and well-being. AI-generated portrayals of characters can feature synthesized faces, bodies and voices of anyone, from a fictional character to a historical figure, or even a deceased family member. These techniques offer novel opportunities for creating interactions with digital portrayals of individuals that can inspire and intrigue us. Advancements in machine learning have recently enabled the hyper-realistic synthesis of prose, images, audio and video data, in what is referred to as artificial intelligence (AI)-generated media.