Kalyan K Sunkavalli

8520972, Aug 27, 2013

Nov 26, 2008

12/323663

Kalyan Sunkavalli - Cambridge MA, US
Sylvain Paris - Boston MA, US
Wojciech Matusik - Lexington MA, US

Adobe Systems Incorporated - San Jose CA

G06K 9/40

382274, 382173, 382302

A computer-implemented method includes segmenting an input image into a plurality of image cues, each image cue representing a unique set of pixels of the input image. For each image cue, the method includes determining a set of image components, wherein each image component is associated with at least one adjustable factor to represent at least one characteristic of the image cue.

20080218516, Sep 11, 2008

Mar 8, 2007

11/683833

Hanspeter Pfister - Arlington MA, US
Wojciech Matusik - Lexington MA, US
Kalyan Sunkavalli - Cambridge MA, US
Szymon M. Rusinkiewicz - Princeton NJ, US

G06T 15/60

345426

A method factorizing a sequence of images acquired of a scene into lighting components. The scene is illuminated by a moving light source. An appearance profile is constructed for each pixel in the sequence of images. The appearance profile is a vector representing intensities of the pixel at instances in time. The appearance profiles are factorized into a shadow component, a skylight component, and a sunlight component.

20110142370, Jun 16, 2011

Dec 10, 2009

12/634699

Neel Suresh Joshi - Seattle WA, US
Sing Bing Kang - Redmond WA, US
Michael F. Cohen - Seattle WA, US
Kalyan Krishna Sunkavalli - Cambridge MA, US

Microsoft Corporation - Redmond WA

G06K 9/60
H04M 1/00

382307, 4555561

A method described herein includes acts of receiving a sequence of images of a scene and receiving an indication of a reference image in the sequence of images. The method further includes an act of automatically assigning one or more weights independently to each pixel in each image in the sequence of images of the scene. Additionally, the method includes an act of automatically generating a composite image based at least in part upon the one or more weights assigned to each pixel in each image in the sequence of images of the scene.

20220343522, Oct 27, 2022

Apr 16, 2021

17/233122

- San Jose CA, US
Zexiang Xu - San Jose CA, US
Kalyan Krishna Sunkavalli - San Jose CA, US
David Jay Kriegman - San Diego CA, US
Ravi Ramamoorthi - Carlsbad CA, US

G06T 7/514
G06T 17/20
H04N 13/111
H04N 13/128
H04N 13/282

Enhanced methods and systems for generating both a geometry model and an optical-reflectance model (an object reconstruction model) for a physical object, based on a sparse set of images of the object under a sparse set of viewpoints. The geometry model is a mesh model that includes a set of vertices representing the object's surface. The reflectance model is SVBRDF that is parameterized via multiple channels (e.g., diffuse albedo, surface-roughness, specular albedo, and surface-normals). For each vertex of the geometry model, the reflectance model includes a value for each of the multiple channels. The object reconstruction model is employed to render graphical representations of a virtualized object (a VO based on the physical object) within a computation-based (e.g., a virtual or immersive) environment. Via the reconstruction model, the VO may be rendered from arbitrary viewpoints and under arbitrary lighting conditions.

20220335636, Oct 20, 2022

Apr 15, 2021

17/231833

- San Jose CA, US
Zexiang Xu - San Jose CA, US
Kalyan Krishna Sunkavalli - San Jose CA, US
Milos Hasan - Oakland CA, US
David Jay Kriegman - San Diego CA, US
Ravi Ramamoorthi - Carlsbad CA, US

G06T 7/586
G06T 9/00
G06T 15/50
G06T 15/06

A scene reconstruction system renders images of a scene with high-quality geometry and appearance and supports view synthesis, relighting, and scene editing. Given a set of input images of a scene, the scene reconstruction system trains a network to learn a volume representation of the scene that includes separate geometry and reflectance parameters. Using the volume representation, the scene reconstruction system can render images of the scene under arbitrary viewing (view synthesis) and lighting (relighting) locations. Additionally, the scene reconstruction system can render images that change the reflectance of objects in the scene (scene editing).

20220335671, Oct 20, 2022

Apr 16, 2021

17/232890

- San Jose CA, US
Kalyan Sunkavalli - San Jose CA, US
I-Ming Pao - Palo Alto CA, US
Guotong Feng - San Jose CA, US
Jianming Zhang - Campbell CA, US
Frederick Mandia - San Jose CA, US

G06T 11/60
G06T 11/40
G06N 3/02

Systems and methods for image editing are described. Embodiments of the present disclosure provide an image editing system for performing image object replacement or image region replacement (e.g., an image editing system for replacing an object or region of an image with an object or region from another image). For example, the image editing system may replace a sky portion of an image with a more desirable sky portion from a different replacement image. According to some embodiments described herein, real-time color harmonization based on the visible sky region may be used to produce more natural colorization. In some examples, horizon-aware sky alignment and placement with advanced padding may also be used. For example, the horizons of the original image and the replacement image may be automatically detected and aligned, and color harmonization may be performed based on the aligned images.

20220335682, Oct 20, 2022

Apr 19, 2021

17/233861

- San Jose CA, US
Kalyan Krishna Sunkavalli - San Jose CA, US
Milos Hasan - Oakland CA, US

G06T 15/50
G06T 15/04
G06N 3/08
G06T 15/00

Methods, system, and computer storage media are provided for generating physical-based materials for rendering digital objects with an appearance of a real-world material. Images depicted the real-world material, including diffuse component images and specular component images, are captured using different lighting patterns, which may include area lights. From the captured images, approximations of one or more material maps are determined using a photometric stereo technique. Based on the approximations and the captured images, a neural network system generates a set of material maps, such as a diffuse albedo material map, a normal material map, a specular albedo material map, and a roughness material map. The material maps from the neural network may be optimized based on a comparison of the input images of the real-world material and images rendered from the material maps.

20220301243, Sep 22, 2022

Mar 17, 2021

17/204638

- SAN JOSE CA, US
Alan Erickson - Franktown CO, US
I-Ming Pao - Palo Aito CA, US
Guotong Feng - San Jose CA, US
Kalyan Sunkavalli - San Jose CA, US
Frederick Mandia - San Jose CA, US
Hyunghwan Byun - San Francisco CA, US
Betty Leong - Los Altos CA, US
Meredith Payne Stotzner - San Jose CA, US
Yukie Takahashi - Newark CA, US
Quynn Megan Le - Morgan Hill CA, US
Sarah Kong - Cupertino CA, US

G06T 11/60
G06T 11/00
G06F 3/0484

The present disclosure provides systems and methods for image editing. Embodiments of the present disclosure provide an image editing system for perform image object replacement or image region replacement (e.g., an image editing system for replacing an object or region of an image with an object or region from another image). For example, the image editing system may replace a sky portion of an image with a more desirable sky portion from a different replacement image. The original image and the replacement image (e.g., the image including a desirable object or region) include layers of masks. A sky from the replacement image may replace the sky of the image to produce an aesthetically pleasing composite image.