Mimicry

Facial expressions are detected using computer vision technology, which involves the use of cameras and algorithms to analyze and interpret human expressions and gestures. The process typically involves capturing an image or video of a person's face, and then using algorithms to determine the position and movement of various facial features such as the eyes, eyebrows, mouth, and cheeks.

This information is then used to control the corresponding features of a 3D avatar, allowing it to reproduce the user's facial expressions in real time. To accomplish this, the technology uses complex mathematical models and algorithms to translate the movement and position of the user's facial features onto the 3D avatar.

Technologically, this process is achieved through the integration of advanced computer vision algorithms and machine learning methods, which are capable of processing massive amounts of data and recognizing patterns in real time. These algorithms work together to precisely track the user's facial movements and translate them into corresponding movements in the 3D avatar.

Additionally, the technology can be trained on large volumes of data, including video recordings of human facial expressions, to improve accuracy and performance over time. This ongoing training process allows the technology to better recognize and reproduce subtle variations in facial expression, ultimately leading to the creation of a more realistic and natural 3D avatar.