公开(公告)号：US20220301243A1

公开(公告)日：2022-09-22

申请号：US17204638

申请日：2021-03-17

Applicant: ADOBE INC.

Inventor： JIANMING ZHANG ,  Alan Erickson ,  I-Ming Pao ,  Guotong Feng ,  Kalyan Sunkavalli ,  Frederick Mandia ,  Hyunghwan Byun ,  Betty Leong ,  Meredith Payne Stotzner ,  Yukie Takahashi ,  Quynn Megan Le ,  Sarah Kong

IPC: G06T11/60 ,  G06T11/00 ,  G06F3/0484

Abstract: 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.

公开(公告)号：US11257284B2

公开(公告)日：2022-02-22

申请号：US15930925

申请日：2020-05-13

Applicant: Adobe Inc.

Inventor： Kalyan Sunkavalli ,  Zexiang Xu ,  Sunil Hadap

IPC: G06K9/00 ,  G06T15/50 ,  G06T19/20 ,  G06T7/70 ,  G06N20/00

Abstract: The present disclosure relates to using an object relighting neural network to generate digital images portraying objects under target lighting directions based on sets of digital images portraying the objects under other lighting directions. For example, in one or more embodiments, the disclosed systems provide a sparse set of input digital images and a target lighting direction to an object relighting neural network. The disclosed systems then utilize the object relighting neural network to generate a target digital image that portrays the object illuminated by the target lighting direction. Using a plurality of target digital images, each portraying a different target lighting direction, the disclosed systems can also generate a modified digital image portraying the object illuminated by a target lighting configuration that comprises a combination of the different target lighting directions.

公开(公告)号：US20210158570A1

公开(公告)日：2021-05-27

申请号：US16692503

申请日：2019-11-22

Applicant: Adobe Inc.

Inventor： Tharun Mohandoss ,  Pulkit Gera ,  Oliver Wang ,  Kartik Sethi ,  Kalyan Sunkavalli ,  Elya Shechtman ,  Chetan Nanda

IPC: G06T7/90 ,  G06K9/00 ,  G06K9/62 ,  H04N9/64

Abstract: This disclosure involves training generative adversarial networks to shot-match two unmatched images in a context-sensitive manner. For example, aspects of the present disclosure include accessing a trained generative adversarial network including a trained generator model and a trained discriminator model. A source image and a reference image may be inputted into the generator model to generate a modified source image. The modified source image and the reference image may be inputted into the discriminator model to determine a likelihood that the modified source image is color-matched with the reference image. The modified source image may be outputted as a shot-match with the reference image in response to determining, using the discriminator model, that the modified source image and the reference image are color-matched.

公开(公告)号：US10565758B2

公开(公告)日：2020-02-18

申请号：US15622711

申请日：2017-06-14

Applicant: Adobe Inc.

Inventor： Sunil Hadap ,  Elya Shechtman ,  Zhixin Shu ,  Kalyan Sunkavalli ,  Mehmet Yumer

IPC: G06K9/62 ,  G06T11/60 ,  G06T5/00

Abstract: Techniques are disclosed for performing manipulation of facial images using an artificial neural network. A facial rendering and generation network and method learns one or more compact, meaningful manifolds of facial appearance, by disentanglement of a facial image into intrinsic facial properties, and enables facial edits by traversing paths of such manifold(s). The facial rendering and generation network is able to handle a much wider range of manipulations including changes to, for example, viewpoint, lighting, expression, and even higher-level attributes like facial hair and age—aspects that cannot be represented using previous models.

公开(公告)号：US20190340810A1

公开(公告)日：2019-11-07

申请号：US15970367

申请日：2018-05-03

Applicant: Adobe Inc.

Inventor： Kalyan Sunkavalli ,  Zexiang Xu ,  Sunil Hadap

IPC: G06T15/50 ,  G06T19/20 ,  G06T7/70 ,  G06F15/18

Abstract: The present disclosure relates to using an object relighting neural network to generate digital images portraying objects under target lighting directions based on sets of digital images portraying the objects under other lighting directions. For example, in one or more embodiments, the disclosed systems provide a sparse set of input digital images and a target lighting direction to an object relighting neural network. The disclosed systems then utilize the object relighting neural network to generate a target digital image that portrays the object illuminated by the target lighting direction. Using a plurality of target digital images, each portraying a different target lighting direction, the disclosed systems can also generate a modified digital image portraying the object illuminated by a target lighting configuration that comprises a combination of the different target lighting directions.

公开(公告)号：US20190164261A1

公开(公告)日：2019-05-30

申请号：US15824943

申请日：2017-11-28

Applicant: Adobe Inc.

Inventor： Kalyan Sunkavalli ,  Mehmet Ersin Yumer ,  Marc-Andre Gardner ,  Xiaohui Shen ,  Jonathan Eisenmann ,  Emiliano Gambaretto

IPC: G06T5/00 ,  G06T9/00 ,  G06T7/90 ,  G06T1/00

CPC classification number: G06T5/007 ,  G06N3/0454 ,  G06N3/082 ,  G06T1/0007 ,  G06T1/20 ,  G06T7/90 ,  G06T9/002 ,  G06T2207/10024 ,  G06T2207/10152 ,  G06T2215/12

Abstract: Systems and techniques for estimating illumination from a single image are provided. An example system may include a neural network. The neural network may include an encoder that is configured to encode an input image into an intermediate representation. The neural network may also include an intensity decoder that is configured to decode the intermediate representation into an output light intensity map. An example intensity decoder is generated by a multi-phase training process that includes a first phase to train a light mask decoder using a set of low dynamic range images and a second phase to adjust parameters of the light mask decoder using a set of high dynamic range image to generate the intensity decoder.