In 1999 Paul Debevec et al. of USC did the first known reflectance capture over the human face with their extremely simple light stage.
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.
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.
[35] Main applications fall within the domains of stock photography, synthetic datasets, virtual cinematography, computer and video games and covert disinformation attacks.
[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 syndrome or social anxiety.
[40] 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).
[43][44] 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.