Lucasfilm Entertainment Company Ltd.

Loomai, Inc.

Roblox Corporation

Kiran Bhat

Michael Koperwas

Will Welch

Ronald Mallet

Steve Sullivan

Kevin Wooley

Cary Phillips

Frederic P Pighin

Vivek Verma

Akash Garg

Sheila Santos

Ji Hun Yu

Michael Daniel Flynn

Ian Sachs

Michael Sanders

Brett Allen

Christopher David Twigg

Rachel Rose

Yuting Ye

Mahesh Kumar Nandwana

Jeffery Bruce Yost

Christoph Bregler

Adrian R Goldenthal

Eloi Du Bois

Dario Kneubuhler

Inaki Navarro

Tijmen Verhulsdonck

Jung-Seung Hong

Emiliano Gambaretto

Morgan Samuel McGuire

Matthew Maverick Loper

Jason Smith

Stephane Grabli

Stéphane Grabli

Jason Snell

Jeffrey Bruce Yost

Curt Isamu Miyashiro

Andrew Johnson

David Brown

Vincent Petrella

Simone Guggiari

Senthil Radhakrishnan

Rishabh Battulwar

Eloi Henri Homere Du Bois

Chong Shang

Tim Harrington

Delio Tramontozzi

Michael Palleschi

Mahesh Ramasubramanian

Dominic Monn

Implementations described herein relate to methods, systems, and computer-readable media to display a virtual character within a virtual environment on a display device. In some implementations, a method can include obtaining an input pose of the virtual character, wherein the virtual character is based on a rig that comprises a plurality of joints, receiving an indication of one or more of a position and an orientation of a target end effector located on the rig, determining an output pose for the rig wherein the determining comprises calculating a respective orientation and position of one or more joints of the plurality of joints of the rig based on the position of the target end effector and rotation constraints of a plurality of joints of a reference rig, and displaying the virtual character in the output pose on the display device.

Determination and display of inverse kinematic poses of virtual characters in a virtual environment

Implementations described herein relate to methods, systems, and computer-readable media to generate animations for a 3D avatar from input video captured at a client device. A camera may capture video of a face while a trained face detection model and a trained regression model output a set of FACS weights, head poses, and facial landmarks to be translated into the animations of the 3D avatar. Additionally, a higher level-of-detail may be intelligently selected based upon user preferences and/or computing conditions at the client device.

Robust facial animation from video using neural networks

A computer-implemented method includes receiving, at a server, a first audio stream of a performance associated with a first client device. The method further includes receiving, at the server, a second audio stream of the performance associated with a second client device. The method further includes during a time window of the performance, where the time window is less than a total time of the performance: generating a synthesized first audio stream that predicts a future of the performance based on audio features of the first audio stream and mixing the synthesized first audio stream and the second audio stream to form a combined audio stream that synchronizes the synthesized first audio stream and the second audio stream, where the time window is advanced and the generating and the mixing are repeated until the performance is complete. The method further includes transmitting the combined audio stream to the second client device.

Synthesizing audio for synchronous communication

A computer-implemented method includes receiving a first audio stream of a performance associated with a first client device. The method further includes during a time window of the performance, wherein the time window is less than a total time of the performance: generating a synthesized first audio stream that predicts a future of the performance based on audio features of the first audio stream and mixing the synthesized first audio stream and a second audio stream associated with a second client device to form a combined audio stream that synchronizes the synthesized first audio stream and the second audio stream, where the time window is advanced and the generating and the mixing are repeated until the performance is complete.

Systems and methods for animating from audio in accordance with embodiments of the invention are illustrated. One embodiment includes a method for generating animation from audio. The method includes steps for receiving input audio data, generating an embedding for the input audio data, and generating several predictions for several tasks from the generated embedding. The several predictions includes at least one of blendshape weights, event detection, and/or voice activity detection. The method includes steps for generating a final prediction from the several predictions, where the final prediction includes a set of blendshape weights, and generating an output based on the generated final prediction.

Systems and methods for animation generation

A method of transferring a facial expression from a subject to a computer generated character that includes: receiving a plate with an image of the subject's facial expression and an estimate of intrinsic parameters of a camera used to film the plate; generating a three-dimensional parameterized deformable model of the subject's face where different facial expressions of the subject can be obtained by varying values of the model parameters; solving for the facial expression in the plate by executing a deformation solver to solve for at least some parameters of the deformable model with a differentiable renderer and shape-from-shading techniques, using as inputs, the three-dimensional parameterized deformable model, estimated intrinsic camera parameters, estimated lighting conditions and albedo estimates over a series of iterations to infer geometry of the facial expression and generate an intermediate facial; generating, from the intermediate facial mesh, refined albedo estimates for the deformable model; and solving for the facial expression in the plate by executing the deformation solver using the intermediate facial mesh, the estimated intrinsic camera parameters, the estimated lighting conditions and the refined albedo estimates as inputs over a series of iterations to infer geometry of the facial expression and generate a final facial mesh using the set of parameter values of the deformable model which result in a facial expression that more closely matches the expression of the subject in the plate than does the intermediate facial mesh.

Facial performance capture in an uncontrolled environment

A system includes a computing device that includes a memory configured to store instructions. The system also includes a processor configured to execute the instructions to perform a method that includes receiving multiple representations of one or more expressions of an object. Each of the representations includes position information attained from one or more images of the object. The method also includes producing an animation model from one or more groups of controls that respectively define each of the one or more expressions of the object as provided by the multiple representations. Each control of each group of controls has an adjustable value that defines the geometry of at least one shape of a portion of the respective expression of the object. Producing the animation model includes producing one or more corrective shapes if the animation model is incapable of accurately presenting the one or more expressions of the object as provided by the multiple representations.

Facial animation models

System and methods for computer animations of 3D models of heads generated from images of faces is disclosed. A 2D captured image that includes an image of a face can be received and used to generate a static 3D model of a head. A rig can be fit to the static 3D model to generate an animation-ready 3D generative model. Sets of rigs can be parameters that each map to particular sounds or particular facial movement observed in a video. These mappings can be used to generate a playlists of sets of rig parameters based upon received audio or video content. The playlist may be played in synchronization with an audio rendition of the audio content. Methods can receive a captured image, identify taxonomy attributes from the captured image, select a template model for the captured image, and perform a shape solve for the selected template model based on the identified taxonomy attributes.

Systems and methods for generating computer ready animation models of a human head from captured data images

A system includes a computing device that includes a memory configured to store instructions. The system also includes a processor configured to execute the instructions to perform a method that includes receiving multiple representations of an object. Each of the representations includes position information of the object and corresponds to an instance in time. For at least one of the representations, the method includes defining a contour that represents a movable silhouette of a surface feature of the object. The method also includes producing a deformable model of the surface of the object from the defined contour and from the at least one representation of the object.

A system includes a computing device that includes a memory and a processor configured to execute instructions to perform a method that includes receiving multiple representations of one or more expressions of an object. Each representation includes position information attained from one or more images of the object. The method also includes producing an animation model from one or more groups of controls that respectively define each of the one or more expressions of the object as provided by the multiple representations. Each control of each group of controls has an adjustable value that defines the geometry of at least one shape of a portion of the respective expression of the object. Producing the animation model includes producing one or more corrective shapes if the animation model is incapable of accurately presenting the one or more expressions of the object as provided by the multiple representations.

Systems and methods for animating expressions of 3D models from captured images of a user's face in accordance with various embodiments of the invention are disclosed. In many embodiments, expressions are identified based on landmarks from images of a user's face. In certain embodiments, weights for morph targets of a 3D model are calculated based on identified landmarks and/or weights for predefined facial expressions to animate expressions for the 3D model.

Methods and systems for animating facial expressions

System and methods for computer animations of 3D models of heads generated from images of faces is disclosed. A 2D captured image that includes an image of a face can be received and used to generate a static 3D model of a head. A rig can be fit to the static 3D model to generate an animation-ready 3D generative model. Sets of rigs can be parameters that each map to particular sounds. These mappings can be used to generate a playlists of sets of rig parameters based upon received audio content. The playlist may be played in synchronization with an audio rendition of the audio content.

Systems and methods for animating models from audio data

Performance capture systems and techniques are provided for capturing a performance of a subject and reproducing an animated performance that tracks the subject's performance. For example, systems and techniques are provided for determining control values for controlling an animation model to define features of a computer-generated representation of a subject based on the performance. A method may include obtaining input data corresponding to a pose performed by the subject, the input data including position information defining positions on a face of the subject. The method may further include obtaining an animation model for the subject that includes adjustable controls that control the animation model to define facial features of the computer-generated representation of the face, and matching one or more of the positions on the face with one or more corresponding positions on the animation model. The matching includes using an objective function to project an error onto a control space of the animation model. The method may further include determining, using the projected error and one or more constraints on the adjustable controls, one or more values for one or more of the adjustable controls. The values are configured to control the animation model to cause the computer-generated representation to perform a representation of the pose using the one or more adjustable controls.

Determining control values of an animation model using performance capture

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San Francisco, CA, United States of America

Kiran Bhat