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[36] [34] Some facial-recognition AI use images generated by other AI as synthetic data for training. [ 37 ] Furthermore, some research suggests that it can have therapeutic effects as " psychologists and counselors have also begun using avatars to deliver therapy to clients who have phobias , a history of trauma , addictions, Asperger’s ...
The website Artbreeder, launched in 2018, uses the models StyleGAN and BigGAN [56] [57] to allow users to generate and modify images such as faces, landscapes, and paintings. [58] In the 2020s, text-to-image models, which generate images based on prompts, became widely used, marking yet another shift in the creation of AI generated artworks. [2]
The similarly AI-driven text adventure game AI Dungeon uses Artbreeder to generate profile pictures for its users, [7] and The Static Age's Andrew Paley has used Artbreeder to create the visuals for his music videos. [8] [9] Artbreeder has been used to create portraits of characters from popular novels such as Harry Potter and Twilight.
According to a test performed by Ars Technica, the outputs generated by Flux.1 Dev and Flux.1 Pro are comparable with DALL-E 3 in terms of prompt fidelity, with the photorealism closely matched Midjourney 6 and generated human hands with more consistency over previous models such as Stable Diffusion XL. [32]
AI generated images have become much more advanced. In March 2020, the release of 15.ai, a free web application created by an anonymous MIT researcher that could generate convincing character voices using minimal training data, marked one of the earliest popular use cases of generative AI. [42]
FaceGen 3.3 allows the user to randomize, tween, normalize and exaggerate faces, and also includes algorithms for adjusting apparent age, ethnicity and gender. It also allows limited parametric control of facial expressions, and includes a set of phoneme expressions for the animation of characters with "speaking" roles.
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The software is designed to detect faces and other patterns in images, with the aim of automatically classifying images. [10] However, once trained, the network can also be run in reverse, being asked to adjust the original image slightly so that a given output neuron (e.g. the one for faces or certain animals) yields a higher confidence score.
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