公开(公告)号：US20250036858A1

公开(公告)日：2025-01-30

申请号：US18225906

申请日：2023-07-25

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Ryan Aponte ,  Shunan Guo ,  Nedim Lipka ,  Jane Hoffswell ,  Chang Xiao ,  Eunyee Koh ,  Yeuk-yin Chan

IPC: G06F40/154 ,  G06F40/117 ,  G06F40/143

Abstract: Techniques discussed herein generally relate to applying machine-learning techniques to design documents to determine relationships among the different style elements within the document. In one example, hypergraph model is trained on a corpus of hypertext markup language (HTML) documents. The trained model is utilized to identifying one or more candidate style elements for a candidate fragment and/or a candidate fragment. Each of the candidates are scored, and at least a portion of the scored candidates are presented as design options for generating a new document.

公开(公告)号：US20240202940A1

公开(公告)日：2024-06-20

申请号：US18084606

申请日：2022-12-20

Applicant: Adobe Inc.

Inventor： Chang Xiao ,  Ryan Rossi ,  Enyu Cai

IPC: G06T7/246 ,  G06T7/215 ,  G06T7/73 ,  G06T15/20

CPC classification number: G06T7/248 ,  G06T7/215 ,  G06T7/74 ,  G06T15/205 ,  G06T2207/10016 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30244 ,  G06T2210/56

Abstract: Certain aspects and features of this disclosure relate to providing a hybrid approach for camera pose estimation using a deep learning-based image matcher and a match refinement procedure. The image matcher takes an image pair as an input and estimates coarse point-to-point feature matches between the two images. The coarse point-to-point feature matches can be filtered based on a stability threshold to produce high-stability point-to-point matches. A perspective-n-point (PnP) camera pose for each frame of video, including one or more added digital visual elements can be computed using the high-stability matches and video frames can be rendered, each using its computed camera pose.

公开(公告)号：US20250036936A1

公开(公告)日：2025-01-30

申请号：US18358502

申请日：2023-07-25

Applicant: ADOBE INC.

Inventor： Ryan A. Rossi ,  Ryan Aponte ,  Shunan Guo ,  Jane Elizabeth Hoffswell ,  Nedim Lipka ,  Chang Xiao ,  Yeuk-yin Chan ,  Eunyee Koh

IPC: G06N3/08

Abstract: A method, apparatus, and non-transitory computer readable medium for hypergraph processing are described. Embodiments of the present disclosure obtain, by a hypergraph component, a hypergraph that includes a plurality of nodes and a hyperedge, wherein the hyperedge connects the plurality of nodes; perform, by a hypergraph neural network, a node hypergraph convolution based on the hypergraph to obtain an updated node embedding for a node of the plurality of nodes; and generate, by the hypergraph component, an augmented hypergraph based on the updated node embedding.

公开(公告)号：US12093322B2

公开(公告)日：2024-09-17

申请号：US17654933

申请日：2022-03-15

Applicant: Adobe Inc.

Inventor： Fayokemi Ojo ,  Ryan Rossi ,  Jane Hoffswell ,  Shunan Guo ,  Fan Du ,  Sungchul Kim ,  Chang Xiao ,  Eunyee Koh

IPC: G06F16/904 ,  G06N3/02

CPC classification number: G06F16/904 ,  G06N3/02

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a graph neural network to generate data recommendations. The disclosed systems generate a digital graph representation comprising user nodes corresponding to users, data attribute nodes corresponding to data attributes, and edges reflecting historical interactions between the users and the data attributes; Moreover, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. In addition, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. Furthermore, the disclosed systems determine a data recommendation for a target user utilizing the data attribute embeddings and a target user embedding corresponding to the target user from the user embeddings.

公开(公告)号：US20230386143A1

公开(公告)日：2023-11-30

申请号：US17664972

申请日：2022-05-25

Applicant: ADOBE INC.

Inventor： Chang Xiao ,  Ryan A. Rossi ,  Eunyee Koh

IPC: G06T19/00 ,  G06T7/00 ,  G06T7/73

CPC classification number: G06T19/006 ,  G06T7/97 ,  G06T7/73 ,  G06T2207/30204 ,  G06T2207/20224

Abstract: A system and methods for providing human-invisible AR markers is described. One aspect of the system and methods includes identifying AR metadata associated with an object in an image; generating AR marker image data based on the AR metadata; generating a first variant of the image by adding the AR marker image data to the image; generating a second variant of the image by subtracting the AR marker image data from the image; and displaying the first variant and the second variant of the image alternately at a display frequency to produce a display of the image, wherein the AR marker image data is invisible to a human vision system in the display of the image.

公开(公告)号：US12125148B2

公开(公告)日：2024-10-22

申请号：US17664972

申请日：2022-05-25

Applicant: ADOBE INC.

Inventor： Chang Xiao ,  Ryan A. Rossi ,  Eunyee Koh

IPC: G06T19/00 ,  G06T7/00 ,  G06T7/73

CPC classification number: G06T19/006 ,  G06T7/73 ,  G06T7/97 ,  G06T2207/20224 ,  G06T2207/30204

Abstract: A system and methods for providing human-invisible AR markers is described. One aspect of the system and methods includes identifying AR metadata associated with an object in an image; generating AR marker image data based on the AR metadata; generating a first variant of the image by adding the AR marker image data to the image; generating a second variant of the image by subtracting the AR marker image data from the image; and displaying the first variant and the second variant of the image alternately at a display frequency to produce a display of the image, wherein the AR marker image data is invisible to a human vision system in the display of the image.

公开(公告)号：US20230297625A1

公开(公告)日：2023-09-21

申请号：US17654933

申请日：2022-03-15

Applicant: Adobe Inc.

Inventor： Fayokemi Ojo ,  Ryan Rossi ,  Jane Hoffswell ,  Shunan Guo ,  Fan Du ,  Sungchul Kim ,  Chang Xiao ,  Eunyee Koh

IPC: G06F16/904 ,  G06N3/02

CPC classification number: G06F16/904 ,  G06N3/02

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a graph neural network to generate data recommendations. The disclosed systems generate a digital graph representation comprising user nodes corresponding to users, data attribute nodes corresponding to data attributes, and edges reflecting historical interactions between the users and the data attributes; Moreover, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. In addition, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. Furthermore, the disclosed systems determine a data recommendation for a target user utilizing the data attribute embeddings and a target user embedding corresponding to the target user from the user embeddings.