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公开(公告)号：US20240202940A1
公开(公告)日：2024-06-20
申请号：US18084606
申请日：2022-12-20
申请人： Adobe Inc.
发明人： Chang Xiao , Ryan Rossi , Enyu Cai
CPC分类号： G06T7/248 , G06T7/215 , G06T7/74 , G06T15/205 , G06T2207/10016 , G06T2207/20081 , G06T2207/20084 , G06T2207/30244 , G06T2210/56
摘要： 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.
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公开(公告)号：US12093322B2
公开(公告)日：2024-09-17
申请号：US17654933
申请日：2022-03-15
申请人： Adobe Inc.
发明人： Fayokemi Ojo , Ryan Rossi , Jane Hoffswell , Shunan Guo , Fan Du , Sungchul Kim , Chang Xiao , Eunyee Koh
IPC分类号： G06F16/904 , G06N3/02
CPC分类号： G06F16/904 , G06N3/02
摘要： 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.
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公开(公告)号：US20230386143A1
公开(公告)日：2023-11-30
申请号：US17664972
申请日：2022-05-25
申请人： ADOBE INC.
发明人： Chang Xiao , Ryan A. Rossi , Eunyee Koh
CPC分类号： G06T19/006 , G06T7/97 , G06T7/73 , G06T2207/30204 , G06T2207/20224
摘要： 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.
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公开(公告)号：US12125148B2
公开(公告)日：2024-10-22
申请号：US17664972
申请日：2022-05-25
申请人： ADOBE INC.
发明人： Chang Xiao , Ryan A. Rossi , Eunyee Koh
CPC分类号： G06T19/006 , G06T7/73 , G06T7/97 , G06T2207/20224 , G06T2207/30204
摘要： 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.
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5.发明公开公开(公告)号：US20230297625A1
公开(公告)日：2023-09-21
申请号：US17654933
申请日：2022-03-15
申请人： Adobe Inc.
发明人： Fayokemi Ojo , Ryan Rossi , Jane Hoffswell , Shunan Guo , Fan Du , Sungchul Kim , Chang Xiao , Eunyee Koh
IPC分类号： G06F16/904 , G06N3/02
CPC分类号： G06F16/904 , G06N3/02
摘要： 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.
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