Qualcomm Mems Technologies, Inc.

Outward, Inc.

Idc, LLC

Iridigm Display Corporation

Schlumberger Technology Corporation

Qualcomm Incorporated

Clarence Chui

Manish Kothari

Manu Parmar

Jeffrey Brian Sampsell

Marc Mignard

William J Cummings

Ming-Hau Tung

Mark W Miles

Brian James Gally

Lauren Palmateer

Mithran C Mathew

Brook Seaton

Teruo Sasagawa

Avinash Venkata Uppuluri

Suryaprakash Ganti

John Batey

Zhanpeng Feng

Lior Kogut

Alan G Lewis

Christopher Murphy

Denis H Endisch

Yeh-Jiun Tung

Chih-Wei Chiang

John Le

Chun-Ming Wang

Andrew Pang

Amogh Subbakrishna Adishesha

Harshul Gupta

James E Brooks

Nolan C Lerche

Stuart B Brown

Mark Todorovich

Stephen Zee

Wilhelmus Johannes Robertus Van Lier

Gaurav Sethi

Wonsuk Chung

Sauri Gudlavalleti

Charles Cameron Abnet

Himanshu Jain

Akshansh Chaudhry

Lakshay Sharma

Jonghae Kim

Evgeni Petrovich Gousev

Matthew Michael Nowak

Je-Hsiung Lan

Ravindra Vaman Shenoy

Philip Don Floyd

Jonathan Charles Griffiths

Richard Alan Stewart

Nassim Khonsari

Hamood-Ur Rehman

Robert S Hastings

Jodhvir Singh Chahal

A content generation platform is generally described herein. More specifically, interactive image generation and techniques and features thereof are disclosed herein. One or more sets of images of a scene are captured in an imaging studio. The captured one or more sets of images of the scene are processed using one or more machine learning based networks to generate an interactive image of the scene comprising a plurality of interactive features. One or more of the plurality of interactive features of the generated interactive image may be modified or edited according to user preferences.

Interactive image generation

A machine learning based image processing and generation framework is disclosed. In some embodiments, depth values of an object or asset in a received input image are at least in part determined using a machine learning based framework that is constrained to a known prescribed environment. Determined depth values facilitate generation of other views of the object or asset.

Arbitrary view generation

Techniques for generating an arbitrary view of a scene from existing images are disclosed. In some embodiments, in response to receiving a request for an image comprising a desired perspective of a scene, the image comprising the desired perspective of the scene is generated using pixels obtained from a plurality of existing images having the desired perspective, wherein one or more of the plurality of existing images are transformed to the desired perspective from other perspectives.

Techniques associated with adaptive sampling are disclosed. In some embodiments, in response to receiving a specification of a scene to render, a sampling of each portion of the scene is determined based at least in part on the specification of the scene such that the scene is not uniformly sampled, and the scene is rendered according to the determined sampling of each portion of the scene.

Adaptive virtual camera sensor

Techniques for generating three-dimensional content from the recordings of one or more independently operated cameras that are not constrained to fixed positions and orientations are disclosed. In some embodiments, data associated with a camera recording a scene is received, wherein camera pose with respect to the scene is not known; camera pose with respect to the scene is determined based on received image data captured by the camera and received sensor data associated with the camera; and the received data and determined camera pose are used to facilitate generation of at least a portion of a point cloud corresponding to the scene.

Multi-camera 3D content creation

Techniques for providing blended physical and virtual reality experiences are disclosed. In some embodiments, a rendering of an actual view of a scene as seen by a camera capturing the scene is obtained from existing assets associated with a virtualized version of the scene and displayed. The actual view of the scene comprises a known environment that includes one or more of a constrained set of objects. The rendering facilitates surfacing information associated with one or more objects comprising the actual view.

Blended physical and virtual realities

A machine learning based image processing architecture and associated applications are disclosed herein. In some embodiments, a machine learning framework is trained to learn low level image attributes such as object/scene types, geometries, placements, materials and textures, camera characteristics, lighting characteristics, contrast, noise statistics, etc. Thereafter, the machine learning framework may be employed to detect such attributes in other images and process the images at the attribute level.

Machine learning based image processing techniques

Two-dimensional compositing that preserves the curvatures of non-flat surfaces is disclosed. In some embodiments, a mapping is associated with a two-dimensional rendering that maps a potentially variable portion of the two-dimensional rendering to a canvas. The mapping is generated from a three-dimensional model of the potentially variable portion of the two-dimensional rendering. The potentially variable portion of the two-dimensional rendering is dynamically modified according to the mapping to reflect content comprising the canvas or edits received with respect to the canvas.

Two-dimensional compositing

Techniques for tagging virtualized content are disclosed. In some embodiments, a modeled three-dimensional scene of objects representing abstracted source content is generated and analyzed to determine a contextual characteristic of the scene that is based on a plurality of objects comprising the scene. The modeled scene is tagged with a tag specifying the determined contextual characteristic.

Tagging virtualized content

Growing community of inventors

San Mateo, CA, United States of America

Clarence Chui