Meta Platforms Technologies, LLC

Facebook Technologies, LLC

Elif Albuz

Peihong Guo

Tong Xiao

Bryan Michael Anenberg

James Allan Booth

Ronit Kassis

Samuel Alan Johnson

Xingze He

Shu Liang

Melinda Ozel

Sidi Fu

Colin Lea

Chad Vernon

Nikita Shulga

In one embodiment, one or more computing systems may access a plurality of images corresponding to a portion of a face of a user. The plurality of images is captured from different viewpoints by a plurality of cameras coupled to an artificial-reality system worn by the user. The one or more computing systems may synthesize, using a machine-learning model, to generate a synthesized image corresponding to the portion of the face of the user. The one or more computing systems may access a three-dimensional (3D) facial model representative of the face of the user and generate a texture image by projecting the synthesized image onto the 3D facial model from a specific camera pose. The one or more computing systems may cause an output image of a facial representation of the user to be rendered using at least the 3D facial model and the texture image.

Camera reprojection for faces

Examples described herein include systems for reconstructing facial image data from partial frame data and landmark data. Systems for generating the partial frame data and landmark data are described. Neural networks may be used to reconstruct the facial image data and/or generate the partial frame data. In this manner, compression of facial image data may be achieved in some examples.

Facial image data generation using partial frame data and landmark data

In one embodiment, one or more computing systems may receive an image of a portion of a face of a first user. The one or more computing systems may access a three-dimensional (3D) facial model representative of the face of the first user. The one or more computing systems may identify one or more facial features captured in the image and determine a camera pose relative to the 3D facial model based on the identified one or more facial features in the image and predetermined feature locations on the 3D facial model. The one or more computing systems may determine a mapping relationship between the image and the 3D facial model by projecting the image of the portion of the face of the first user onto the 3D facial model from the camera pose and cause an output image of a facial representation of the first user to be rendered.

An avatar personalization engine can generate personalized avatars for a user by creating a 3D user model based on one or more images of the user. The avatar personalization engine can compute a delta between the 3D user model and an average person model, which is a model created based on the average measurements from multiple people. The avatar personalization engine can then apply the delta to a generic avatar model by changing measurements of particular features of the generic avatar model by amounts specified for corresponding features identified in the delta. This personalizes the generic avatar model to resemble the user. Additional features matching characteristics of the user can be added to further personalize the avatar model, such as a hairstyle, eyebrow geometry, facial hair, glasses, etc.

Avatar fidelity and personalization

Disclosed herein includes a system, a method, and a non-transitory computer readable medium for applying an expression to an avatar. In one aspect, a class of an expression of a face can be determined according to a set of attributes indicating states of portions of the face. In one aspect, a set of blendshapes with respective weights corresponding to the expression of the face can be determined according to the class of the expression of the face. In one aspect, the set of blendshapes with respective weights can be provided as an input to train a machine learning model. In one aspect, the machine learning model can be configured, via training, to generate an output set of blendshapes with respective weights, according to an input image. An image of an avatar may be rendered according to the output set of blendshapes with respective weights.

System and method for applying an expression to an avatar

In one embodiment, a method includes receiving an audio signal comprising a plurality of speech units, processing the audio signal to associate each of the speech units with a corresponding lip animation, determining pitch information associated with each of the plurality of speech units, processing the pitch information of each of the plurality of speech units to associate at least one of the speech units with a facial-component animation, and presenting the audio signal with a displayed animation of a face, wherein the animation of the face displays the lip animation associated with each of the speech units and the facial-component animation associated with the at least one speech unit. The animation of the face may be displayed in real time with the audio signal. The facial component animation may include animation of the lips, eyebrows, eyelids, and other portion of the upper face.

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Los Gatos, CA, United States of America

Elif Albuz