Apple has been granted a patent for “Avatar Modeling and Generation.” It involves the company’s and Animoji and Memoji — and how to individually personalize them.
Animojis allow a user to choose an avatar (e.g., a puppet) to represent themselves. The Animoji can move and talk as if it were a video of the user. Animojis enable users to create personalized versions of emojis in a fun and creative way, and Memoji is the name used for Apple’s personalized “Animoji” characters that can be created and customized right within Messages by choosing from a set of inclusive and diverse characteristics to form a unique personality.
About the patent
The patent relates generally to electronic communication and specifically to techniques and systems for video communication using high-fidelity avatars — that is, computerized characters that represent and are controlled by users. Avatars may take a wide variety of forms including virtual humans, animals, and plant life.
Some computer products include avatars with facial expressions that are driven by a user’s facial expressions. One use of facially-based avatars is in communication, where a camera and microphone in a first device transmits audio and real-time 2D or 3D avatar of a first user to one or more second users such as other mobile devices, desktop computers, videoconferencing systems and the like.
Apple says that known existing systems tend to be computationally intensive, requiring high-performance general and graphics processors, and generally do not work well on mobile devices, such as smartphones or computing tablets. What’s more, existing avatar systems don’t generally provide the ability to communicate nuanced facial representations or emotional states, the tech giant says.
Apple’s patent involves methods for overcoming such limitations.
Summary of the patent
Here’s Apple’s abstract of the patent: “Techniques are disclosed for providing an avatar personalized for a specific person based on known data from a relatively large population of individuals and a relatively small data sample of the specific person. Auto-encoder neural networks are used in a novel manner to capture latent-variable representations of facial models. Once such models are developed, a very limited data sample of a specific person may be used in combination with convolutional-neural-networks or statistical filters, and driven by audio/visual input during real-time operations, to generate a realistic avatar of the specific individual’s face. In some embodiments, conditional variables may be encoded (e.g. gender, age, body-mass-index, ethnicity, emotional state). In other embodiments, different portions of a face may be modeled separately and combined at run-time (e.g., face, tongue and lips). Models in accordance with this disclosure may be used to generate resolution independent output.”
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