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1.发明公开公开(公告)号：US20230325992A1
公开(公告)日：2023-10-12
申请号：US17658774
申请日：2022-04-11
Applicant: Adobe Inc.
Inventor： Zhe Lin , Sijie Zhu , Jason Wen Yong Kuen , Scott Cohen , Zhifei Zhang
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilizes artificial intelligence to learn to recommend foreground object images for use in generating composite images based on geometry and/or lighting features. For instance, in one or more embodiments, the disclosed systems transform a foreground object image corresponding to a background image using at least one of a geometry transformation or a lighting transformation. The disclosed systems further generating predicted embeddings for the background image, the foreground object image, and the transformed foreground object image within a geometry-lighting-sensitive embedding space utilizing a geometry-lighting-aware neural network. Using a loss determined from the predicted embeddings, the disclosed systems update parameters of the geometry-lighting-aware neural network. The disclosed systems further provide a variety of efficient user interfaces for generating composite digital images.
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2.发明公开公开(公告)号：US20230325991A1
公开(公告)日：2023-10-12
申请号：US17658770
申请日：2022-04-11
Applicant: Adobe Inc.
Inventor： Zhe Lin , Sijie Zhu , Jason Wen Yong Kuen , Scott Cohen , Zhifei Zhang
CPC classification number: G06T5/50 , G06T7/194 , G06T5/002 , G06T3/60 , G06T2207/20084 , G06T2207/20221
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilizes artificial intelligence to learn to recommend foreground object images for use in generating composite images based on geometry and/or lighting features. For instance, in one or more embodiments, the disclosed systems transform a foreground object image corresponding to a background image using at least one of a geometry transformation or a lighting transformation. The disclosed systems further generating predicted embeddings for the background image, the foreground object image, and the transformed foreground object image within a geometry-lighting-sensitive embedding space utilizing a geometry-lighting-aware neural network. Using a loss determined from the predicted embeddings, the disclosed systems update parameters of the geometry-lighting-aware neural network. The disclosed systems further provide a variety of efficient user interfaces for generating composite digital images.
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