A method of operating a virtual image generation system comprises allowing an end user to interact with a three-dimensional environment comprising at least one virtual object, presenting a stimulus to the end user in the context of the three-dimensional environment, sensing at least one biometric parameter of the end user in response to the presentation of the stimulus to the end user, generating biometric data for each of the sensed biometric parameter(s), determining if the end user is in at least one specific emotional state based on the biometric data for the each of the sensed biometric parameter(s), and performing an action discernible to the end user to facilitate a current objective at least partially based on if it is determined that the end user is in the specific emotional state(s).

A method and an apparatus of transferring an image, a method of training an image transfer model, a device, and a medium. The method of transferring the image includes: extracting a first attribute feature of a first object and a first shape feature of a target part of the first object respectively according to a first image and a first position information of the target part of the first object in the first image; extracting a first identity feature of a second object contained in a second image; and generating a first transferred image according to the first attribute feature, the first shape feature and the first identity feature, wherein the first transferred image contains the second object having the first attribute feature and the first shape feature.

Systems and methods for educational analysis optimization. The system includes a camera, a processor and memory. The memory stores instructions to execute a method. The method begins with receiving a request from a user at a client device to begin a stimulus session. Then, video recording of the user for the stimulus session is initialized. Next, calibrations for emotions and gaze are set. Then, one or more stimuli are presented to the user. Cues and reactions are recorded and mapped to content that was displayed during the times of recorded reactions and cues. The recordings are post-processed for educational analysis and feedback is provided to the user. The feedback and analysis can be optimized using a predictive artificial intelligence model.

Provided is a method of generating a three-dimensional (3D) avatar from a two-dimensional (2D) image. The method may include obtaining a 2D image by capturing a face of a person, detecting a landmark of the face in the obtained 2D image, generating a first mesh model by modeling a 3D geometrical structure of the face based on the detected landmark, extracting face texture information from the obtained 2D image, determining a second mesh model to be blended with the first mesh model in response to a user input, wherein the first mesh model and the second mesh model have the same mesh topology, generating a 3D avatar by blending the first mesh model and the second mesh model, and applying, to the 3D avatar, a visual expression corresponding to the extracted face texture information.

An expression recognition method is described that includes acquiring a face image to be recognized, and inputting the face image into N different recognition models arranged in sequence for expression recognition and outputting an actual expression recognition result, the N different recognition models being configured to recognize different target expression types, wherein N is an integer greater than 1.

Systems and methods are described for presenting identifiers for media assets recommended to users identified using facial recognition. Each of a first and a second user in a vicinity of user equipment is identified, and a first recommended media asset and a second media asset are determined based on respective user profiles of the first user and the second user. A first identifier selectable to access the first recommended media asset and a second identifier selectable to access the second recommended media asset are generated for display, and a recommended media asset associated with a selected identifier is generated for display.

A method, apparatus, and non-transitory computer readable medium for image processing are described. Embodiments of the method, apparatus, and non-transitory computer readable medium include identifying an original image including a plurality of semantic attributes, wherein each of the semantic attributes represents a complex set of features of the original image; identifying a target attribute value that indicates a change to a target attribute of the semantic attributes; computing a modified feature vector based on the target attribute value, wherein the modified feature vector incorporates the change to the target attribute while holding at least one preserved attribute of the semantic attributes substantially unchanged; and generating a modified image based on the modified feature vector, wherein the modified image includes the change to the target attribute and retains the at least one preserved attribute from the original image.

A method and system for detecting facial expressions in digital images and applications therefore are disclosed. Analysis of a digital image determines whether or not a smile and/or blink is present on a person's face. Face recognition, and/or a pose or illumination condition determination, permits application of a specific, relatively small classifier cascade.

A voice input device provided with an input section for inputting a voice of a user, a recognition section for recognizing the voice of the user inputted by the input section, a generation section for generating characters or a command based on a recognition result of the recognition section, a detection section for detecting a device's own posture, and an instruction section for instructing the generation section to generate the command when a detection result of the detection section represents a specific posture as compared to instructing the generation section to generate the characters when the detection result of the detection section represents a posture other than the specific posture. Accordingly, character input and command input during dictation is correctly distinguished, or more specifically unexpected character input during dictation is avoided.

A mobile device can implement a neural network-based style transfer scheme to modify an image in a first style to a second style. The style transfer scheme can be configured to detect an object in the image, apply an effect to the image, and blend the image using color space adjustments and blending schemes to generate a realistic result image. The style transfer scheme can further be configured to efficiently execute on the constrained device by removing operational layers based on resources available on the mobile device.