公开(公告)号：US11328523B2

公开(公告)日：2022-05-10

申请号：US16897068

申请日：2020-06-09

Applicant: Adobe Inc.

Inventor： Elya Shechtman ,  Oliver Wang ,  Mehmet Yumer ,  Chen-Hsuan Lin

IPC: G06V30/194 ,  G06N3/04 ,  G06N3/08

Abstract: The present disclosure relates to an image composite system that employs a generative adversarial network to generate realistic composite images. For example, in one or more embodiments, the image composite system trains a geometric prediction neural network using an adversarial discrimination neural network to learn warp parameters that provide correct geometric alignment of foreground objects with respect to a background image. Once trained, the determined warp parameters provide realistic geometric corrections to foreground objects such that the warped foreground objects appear to blend into background images naturally when composited together.

公开(公告)号：US11314969B2

公开(公告)日：2022-04-26

申请号：US16777258

申请日：2020-01-30

Applicant: Adobe Inc.

Inventor： Xiao Yang ,  Paul Asente ,  Mehmet Yumer

IPC: G06K9/00 ,  G06K9/62 ,  G06K9/34 ,  G06N3/08 ,  G06K9/32 ,  G06N3/04

Abstract: Disclosed systems and methods categorize text regions of an electronic document into document object types based on a combination of semantic information and appearance information from the electronic document. A page segmentation application executing on a computing device provides a textual feature representation and a visual feature representation to a neural network. The application identifies a correspondence between a location of the set of pixels in the electronic document and a location of a particular document object type in an output page segmentation. The application further outputs a classification of the set of pixels as being the particular document object type based on the identified correspondence.

公开(公告)号：US20200320715A1

公开(公告)日：2020-10-08

申请号：US16907663

申请日：2020-06-22

Applicant: ADOBE INC.

Inventor： Vladimir Kim ,  Aaron Hertzmann ,  Mehmet Yumer

IPC: G06T7/143 ,  G06K9/68 ,  G06K9/34 ,  G06K9/00 ,  G06T7/11

Abstract: The present disclosure includes methods and systems for identifying and manipulating a segment of a three-dimensional digital model based on soft classification of the three-dimensional digital model. In particular, one or more embodiments of the disclosed systems and methods identify a soft classification of a digital model and utilize the soft classification to tune segmentation algorithms. For example, the disclosed systems and methods can utilize a soft classification to select a segmentation algorithm from a plurality of segmentation algorithms, to combine segmentation parameters from a plurality of segmentation algorithms, and/or to identify input parameters for a segmentation algorithm. The disclosed systems and methods can utilize the tuned segmentation algorithms to accurately and efficiently identify a segment of a three-dimensional digital model.

公开(公告)号：US10706554B2

公开(公告)日：2020-07-07

申请号：US15487813

申请日：2017-04-14

Applicant: Adobe Inc.

Inventor： Vladimir Kim ,  Aaron Hertzmann ,  Mehmet Yumer

IPC: G06T7/143 ,  G06K9/68 ,  G06K9/34 ,  G06K9/00 ,  G06T7/11

Abstract: The present disclosure includes methods and systems for identifying and manipulating a segment of a three-dimensional digital model based on soft classification of the three-dimensional digital model. In particular, one or more embodiments of the disclosed systems and methods identify a soft classification of a digital model and utilize the soft classification to tune segmentation algorithms. For example, the disclosed systems and methods can utilize a soft classification to select a segmentation algorithm from a plurality of segmentation algorithms, to combine segmentation parameters from a plurality of segmentation algorithms, and/or to identify input parameters for a segmentation algorithm. The disclosed systems and methods can utilize the tuned segmentation algorithms to accurately and efficiently identify a segment of a three-dimensional digital model.

公开(公告)号：US10692265B2

公开(公告)日：2020-06-23

申请号：US16676733

申请日：2019-11-07

Applicant: Adobe Inc.

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

IPC: G06K9/62 ,  G06T11/60 ,  G06T5/00 ,  G06N3/04 ,  G06N3/08

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：US20200090389A1

公开(公告)日：2020-03-19

申请号：US16676733

申请日：2019-11-07

Applicant: Adobe Inc.

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

IPC: G06T11/60 ,  G06N3/04 ,  G06N3/08 ,  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.