Human image synthesis

Human image synthesis is technology that can be applied to make believable and even photorealistic renditions of human-likenesses, moving or still. It has effectively existed since the early 2000s. Many films using computer generated imagery have featured synthetic images of human-like characters digitally composited onto the real or other simulated film material. Towards the end of the 2010s deep learning artificial intelligence has been applied to synthesize images and video that look like humans, without need for human assistance, once the training phase has been completed, whereas the old school 7D-route required massive amounts of human work.
Key breakthrough to photorealism: reflectance capture.
In 1999 Paul Debevec et al. of USC did the first known reflectance capture over the human face with their extremely simple light stage. They presented their method and results in SIGGRAPH 2000.
The scientific breakthrough required finding the subsurface light component (the simulation models are glowing from within slightly) which can be found using knowledge that light that is reflected from the oil-to-air layer retains its polarization and the subsurface light loses its polarization. So equipped only with a movable light source, movable video camera, 2 polarizers and a computer program doing extremely simple math and the last piece required to reach photorealism was acquired.
For a believable result both light reflected from skin (BRDF) and within the skin (a special case of BTDF) which together make up the BSDF must be captured and simulated.
Synthesis.
The whole process of making digital look-alikes i.e. characters so lifelike and realistic that they can be passed off as pictures of humans is a very complex task as it requires photorealistically modeling, animating, cross-mapping, and rendering the soft body dynamics of the human appearance.
Synthesis with an actor and suitable algorithms is applied using powerful computers. The actor's part in the synthesis is to take care of mimicking human expressions in still picture synthesizing and also human movement in motion picture synthesizing. Algorithms are needed to simulate laws of physics and physiology and to map the models and their appearance, movements and interaction accordingly.
Often both physics/physiology based (i.e. skeletal animation) and image-based modeling and rendering are employed in the synthesis part. Hybrid models employing both approaches have shown best results in realism and ease-of-use. Morph target animation reduces the workload by giving higher level control, where different facial expressions are defined as deformations of the model, which facial allows expressions to be tuned intuitively. Morph target animation can then morph the model between different defined facial expressions or body poses without much need for human intervention.
Using displacement mapping plays an important part in getting a realistic result with fine detail of skin such as pores and wrinkles as small as 100 µm.
Machine learning approach.
In the late 2010s, machine learning, and more precisely generative adversarial networks (GAN), were used by NVIDIA to produce random yet photorealistic human-like portraits. The system, named StyleGAN, was trained on a database of 70,000 images from the images depository website Flickr. The source code was made public on GitHub in 2019. Outputs of the generator network from random input were made publicly available on a number of websites.
Similarly, since 2018, deepfake technology has allowed GANs to swap faces between actors; combined with the ability to fake voices, GANs can thus generate fake videos that seem convincing.
Applications.
Main applications fall within the domains of stock photography, synthetic datasets, virtual cinematography, computer and video games and covert disinformation attacks.
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 syndrome or social anxiety." The strong memory imprint and brain activation effects caused by watching a digital look-alike avatar of yourself is dubbed the doppelgänger effect. The doppelgänger effect can heal when covert disinformation attack is exposed as such to the targets of the attack.
Related issues.
The speech synthesis has been verging on being completely indistinguishable from a recording of a real human's voice since the 2016 introduction of the voice editing and generation software Adobe Voco, a prototype slated to be a part of the Adobe Creative Suite and DeepMind WaveNet, a prototype from Google.
Ability to steal and manipulate other peoples voices raises obvious ethical concerns.
At the 2018 Conference on Neural Information Processing Systems (NeurIPS) researchers from Google presented the work 'Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis', which transfers learning from speaker verification to achieve text-to-speech synthesis, that can be made to sound almost like anybody from a speech sample of only 5 seconds (listen).
Sourcing images for AI training raises a question of privacy as people who are used for training didn't consent.
Digital sound-alikes technology found its way to the hands of criminals as in 2019 Symantec researchers knew of 3 cases where technology has been used for crime.
This coupled with the fact that (as of 2016) techniques which allow near real-time counterfeiting of facial expressions in existing 2D video have been believably demonstrated increases the stress on the disinformation situation.