Snap Inc.

Amazon Technologies, Inc.

Gurunandan Krishnan Gorumkonda

Shree Kumar Nayar

Yicheng Wu

Marian Pho

Karl Bayer

Brian Anthony Smith

Georgios Evangelidis

Hsin-Ying Lee

Ramzi Zahreddine

Jian Wang

Brevin Tilmon

Sizhuo Ma

Bing Zhou

Jie Xu

James Christopher Curlander

Toby Ge Xu

Robert Alexander Colburn

Sean C Murphy

Lilian De Greef

Chenhan Xu

Riku Arakawa

Supreeth Narasimhaswamy

Numair Khalil Ullah Khan

An energy-efficient adaptive 3D sensing system. The adaptive 3D sensing system includes one or more cameras and one or more projectors. The adaptive 3D sensing system captures images of a real-world scene using the one or more cameras and computes depth estimates and depth estimate confidence values for pixels of the images. The adaptive 3D sensing system computes an attention mask based on the one or more depth estimate confidence values and commands the one or more projectors to send a distributed laser beam into one or more areas of the real-world scene based on the attention mask. The adaptive 3D sensing system captures 3D sensing image data of the one or more areas of the real-world scene and generates 3D sensing data for the real-world scene based on the 3D sensing image data.

Energy-efficient adaptive 3D sensing

A messaging system for audio character type swapping. Methods of audio character type swapping include receiving input audio data having a first characteristic and transforming the input audio data to an input image where the input image represents the frequencies and intensities of the audio. The methods further include processing the input image using a convolutional neural network (CNN) to generate an output image and transforming the output image to output audio data, the output audio data having a second characteristic. The input audio and output audio may include vocals. The first characteristics may indicate a male voice and the second characteristics may indicate a female voice. The CNN is trained together with another CNN that changes input audio having the second characteristic to audio having the first characteristic. The CNNs are trained using discriminator CNNs that determine whether audio has a first characteristic or a second characteristic.

Neural networks for changing characteristics of vocals

Methods, devices, media, and other embodiments are described for managing and configuring a pseudorandom animation system and associated computer animation models. One embodiment involves generating image modification data with a computer animation model configured to modify frames of a video image to insert and animate the computer animation model within the frames of the video image, where the computer animation model of the image modification data comprises one or more control points. Motion patterns and speed harmonics are automatically associated with the control points, and motion states are generated based on the associated motions and harmonics. A probability value is then assigned to each motion state. The motion state probabilities can then be used when generating a pseudorandom animation.

Management of pseudorandom animation system

A finger gesture recognition system is provided. The finger gesture recognition system includes one or more audio sensors and one or more optic sensors. The finger gesture recognition system captures, using the one or more audio sensors, audio signal data of a finger gesture being made by a user, and captures, using the one or more optic sensors, optic signal data of the finger gesture. The finger gesture recognition system recognizes the finger gesture based on the audio signal data and the optic signal data and communicates finger gesture data of the recognized finger gesture to an Augmented Reality/Combined Reality/Virtual Reality (XR) application.

Finger gesture recognition via acoustic-optic sensor fusion

A device that measures a size of a user's face, referred to as face scaling, using a monocular camera. Depth is calculated from sparse feature points. A face mesh is used to improve the estimation accuracy. A processing pipeline detects face features by applying a face landmark detection algorithm to find the important face feature points such as the eyes, nose, and mouth. The processing pipeline estimates feature points depth using depth obtained through image defocus. The processing pipeline further scales the face using an estimated depth of the face features.

Monocular camera defocus face measuring

A pose tracking system is provided. The pose tracking system includes an EMF tracking system having a user-worn head-mounted EMF source and one or more user-worn EMF tracking sensors attached to the wrists of the user. The EMF source is associated with a VIO tracking system such as AR glasses or the like. The pose tracking system determines a pose of the user's head and a ground plane using the VIO tracking system and a pose of the user's hands using the EMF tracking system to determine a full-body pose for the user. Metal interference with the EMF tracking system is minimized using an IMU mounted with the EMF tracking sensors. Long term drift in the IMU and the VIO tracking system are minimized using the EMF tracking system.

Egocentric human body pose tracking

Methods, devices, media, and other embodiments are described for generating, modifying, and outputting pseudorandom animations that can be synchronized to audio data. In one embodiment, a computer animation model made up of comprising one or more control points is accessed by one or more processors, which associate motion patterns with a first control point of the one or more control points, and associate one or more speed harmonics with the first control point. A set of motion states is identify with a motion state for the combinations of possibilities, and a probability value is assigned to each motion state of the set of motion states. The probability value can be used to probabilistically determine a particular motion state to be part of displayed animation for the computer animation model.

Automated dance animation

Example methods for generating an animated character in dance poses to music may include generating, by at least one processor, a music input signal based on an acoustic signal associated with the music, and receiving, by the at least one processor, a model output signal from an encoding neural network. A current generated pose data is generated using a decoding neural network, the current generated pose data being based on previous generated pose data of a previous generated pose, the music input signal, and the model output signal. An animated character is generated based on a current generated pose data; and the animated character caused to be displayed by a display device.

Music reactive animation of human characters

Shortcut keypad system for electronic communications comprises first apparatus and second apparatus. First apparatus and second apparatus each comprise an input device, a processor and a memory. The input device comprises selectable items including first selectable item. The processor of the first apparatus receives a selection of the first selectable item, and transmits a signal corresponding to the first selectable item to the second apparatus. The processor of the second apparatus receives the signal corresponding to the first selectable item, and causes the input device of the second apparatus to indicate that the signal corresponding to the first selectable item is received. Other embodiments are described herein.

Shortcut keypad for visual electronic communications

An audio response system can generate multimodal messages that can be dynamically updated on viewer's client device based on a type of audio response detected. The audio responses can include keywords or continuum-based signal (e.g., levels of wind noise). A machine learning scheme can be trained to output classification data from the audio response data for content selection and dynamic display updates.

Audio response messages

Method to retrieve media content items using captured parameters associated with a moment starts with a processor detecting an activation of a selectable element. Processor determines parameters associated with the activation including a date and a time that the activation is detected and a location of the selectable element when the activation is detected. Processor stores in a memory of a client device a first record including the parameters. When a request for the first record is received, processor transmits the request to a server for media content items associated with at least one of the parameters of the first record. Media content items are uploaded to the server from client devices communicatively coupled to the server. Processor causes a display of the client device to display the media content items. Other embodiments are described herein.

Selectable element to retrieve media content items

Images of a scene are received. The images represent viewpoints corresponding to the scene. A pixel map of the scene is computed based on the plurality of images. Multi-plane image (MPI) layers from the pixel map are extracted in real-time. The MPI layers are aggregated. The scene is rendered from a novel viewpoint based on the aggregated MPI layers.

High-definition real-time view synthesis

Methods, devices, media, and other embodiments are described for generating pseudorandom animations matched to audio data on a device. In one embodiment a video is generated and output on a display of the device using a computer animation model. Audio is detected from a microphone of the device, and the audio data is processed to determine a set of audio characteristics for the audio data received at the microphone of the device. A first motion state is randomly selected from the plurality of motion states, one or more motion values of the first motion state are generated using the set of audio characteristics, and the video is updated using the one or more motion values with the computer animation model to create an animated action within the video.

Matching audio to a state-space model for pseudorandom animation

An eXtended Reality (XR) system provides grasp detection of a user grasping a virtual object. The grasp detection may be used as a user input into an XR application. The XR system provides a user interface of the XR application to a user of the XR system, the user interface including one or more virtual objects. The XR system captures video frame tracking data of a pose of a hand of a user while the user interacts with a virtual object of the one or more virtual objects and generates skeletal model data of the hand of the user based on the video frame tracking data. XR system generates grasp detection data based on the skeletal model data and virtual object data of the virtual object, and provides the grasp detection data to the XR application as user input into the XR application.

Growing community of inventors

Seattle, WA, United States of America

Gurunandan Krishnan Gorumkonda