Snap Inc.

Yingying Wang

Yuncheng Li

Zehao Xue

Zhou Ren

Liuhao Ge

Chen Cao

Harrison Jesse Smith

Jonathan M Rodriguez, Ii

Bradley Kotsopoulos

Michael Kozakov

Nicholas Hendriks

Derek Spencer

Method for generating media content items on demand starts with a processor receiving an animation file including a first metadata based on an animation input. The animation file is associated with a media content identification. The processor generates puppets associated with frames in the animation file using the first metadata. The processor causes a puppet matching interface to be displayed on a client device. The puppet matching interface includes one of the puppets in a first pose. The processor receives a puppet posing input associated with a second pose from the client device. The processor causes the one of the puppets to be displayed in the second pose in the puppet matching interface by the client device. The processor can also generate a second metadata based on the puppet posing input. Other embodiments are disclosed herein.

System for generating media content items on demand

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing at least one program and a method for transforming a motion style of an avatar from a first style to a second style. The program and method include: retrieving, by a processor from a storage device, an avatar depicting motion in a first style; receiving user input selecting a second style; obtaining, based on the user input, a trained machine learning model that performs a non-linear transformation of motion from the first style to the second style; and applying the obtained trained machine learning model to the retrieved avatar to transform the avatar from depicting motion in the first style to depicting motion in the second style.

Avatar style transformation using neural networks

Systems and methods herein describe using a neural network to identify a first set of joint location coordinates and a second set of joint location coordinates and identifying a three-dimensional hand pose based on both the first and second sets of joint location coordinates.

Hand pose estimation from stereo cameras

Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and a method for receiving a monocular image that includes a depiction of a hand and extracting features of the monocular image using a plurality of machine learning techniques. The program and method further include modeling, based on the extracted features, a pose of the hand depicted in the monocular image by adjusting skeletal joint positions of a three-dimensional (3D) hand mesh using a trained graph convolutional neural network (CNN); modeling, based on the extracted features, a shape of the hand in the monocular image by adjusting blend shape values of the 3D hand mesh representing surface features of the hand depicted in the monocular image using the trained graph CNN; and generating, for display, the 3D hand mesh adjusted to model the pose and shape of the hand depicted in the monocular image.

3D hand shape and pose estimation

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Marina Del Rey, CA, United States of America

Yingying Wang