公开(公告)号：US12154196B2

公开(公告)日：2024-11-26

申请号：US17810392

申请日：2022-07-01

Applicant: Adobe Inc.

Inventor： Zhifei Zhang ,  Zhe Lin ,  Scott Cohen ,  Darshan Prasad ,  Zhihong Ding

IPC: G06T11/40 ,  G06T5/77 ,  G06T7/12

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for transferring global style features between digital images utilizing one or more machine learning models or neural networks. In particular, in one or more embodiments, the disclosed systems receive a request to transfer a global style from a source digital image to a target digital image, identify at least one target object within the target digital image, and transfer the global style from the source digital image to the target digital image while maintaining an object style of the at least one target object.

公开(公告)号：US12148074B2

公开(公告)日：2024-11-19

申请号：US17503671

申请日：2021-10-18

Applicant: Adobe Inc.

Inventor： He Zhang ,  Jeya Maria Jose Valanarasu ,  Jianming Zhang ,  Jose Ignacio Echevarria Vallespi ,  Kalyan Sunkavalli ,  Yilin Wang ,  Yinglan Ma ,  Zhe Lin ,  Zijun Wei

IPC: G06T11/60 ,  G06F3/04842 ,  G06F3/04845 ,  G06N3/08 ,  G06V10/40 ,  G06V10/75

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for accurately and flexibly generating harmonized digital images utilizing an object-to-object harmonization neural network. For example, the disclosed systems implement, and learn parameters for, an object-to-object harmonization neural network to combine a style code from a reference object with features extracted from a target object. Indeed, the disclosed systems extract a style code from a reference object utilizing a style encoder neural network. In addition, the disclosed systems generate a harmonized target object by applying the style code of the reference object to a target object utilizing an object-to-object harmonization neural network.

公开(公告)号：US12112537B2

公开(公告)日：2024-10-08

申请号：US18487183

申请日：2023-10-16

Applicant: ADOBE INC.

Inventor： Quan Hung Tran ,  Long Thanh Mai ,  Zhe Lin ,  Zhuowan Li

IPC: G06V20/30 ,  G06F16/535 ,  G06F16/55 ,  G06F18/214 ,  G06F40/205 ,  G06V10/75 ,  G06V10/82

CPC classification number: G06V20/30 ,  G06F16/535 ,  G06F16/55 ,  G06F18/214 ,  G06F40/205 ,  G06V10/751 ,  G06V10/82

Abstract: A group captioning system includes computing hardware, software, and/or firmware components in support of the enhanced group captioning contemplated herein. In operation, the system generates a target embedding for a group of target images, as well as a reference embedding for a group of reference images. The system identifies information in-common between the group of target images and the group of reference images and removes the joint information from the target embedding and the reference embedding. The result is a contrastive group embedding that includes a contrastive target embedding and a contrastive reference embedding with which to construct a contrastive group embedding, which is then input to a model to obtain a group caption for the target group of images.

公开(公告)号：US20240331214A1

公开(公告)日：2024-10-03

申请号：US18610861

申请日：2024-03-20

Applicant: ADOBE INC.

Inventor： Yuqian Zhou ,  Elya Shechtman ,  Zhe Lin ,  Krishna Kumar Singh ,  Jingwan Lu ,  Connelly Stuart Barnes ,  Sohrab Amirghodsi

IPC: G06T11/00 ,  G06T3/4046 ,  G06T5/30

CPC classification number: G06T11/00 ,  G06T3/4046 ,  G06T5/30 ,  G06T2200/24 ,  G06T2207/20084

Abstract: Systems and methods for image processing (e.g., image extension or image uncropping) using neural networks are described. One or more aspects include obtaining an image (e.g., a source image, a user provided image, etc.) having an initial aspect ratio, and identifying a target aspect ratio (e.g., via user input) that is different from the initial aspect ratio. The image may be positioned in an image frame having the target aspect ratio, where the image frame includes an image region containing the image and one or more extended regions outside the boundaries of the image. An extended image may be generated (e.g., using a generative neural network), where the extended image includes the image in the image region as well as generated image portions in the extended regions and the one or more generated image portions comprise an extension of a scene element depicted in the image.

公开(公告)号：US20240320872A1

公开(公告)日：2024-09-26

申请号：US18426763

申请日：2024-01-30

Applicant: ADOBE INC.

Inventor： Tobias Hinz ,  Venkata Naveen Kumar Yadav Marri ,  Midhun Harikumar ,  Ajinkya Gorakhnath Kale ,  Zhe Lin ,  Oliver Wang ,  Jingwan Lu

IPC: G06T11/00 ,  G06F40/284 ,  G06F40/40

CPC classification number: G06T11/00 ,  G06F40/284 ,  G06F40/40 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: A method, apparatus, non-transitory computer readable medium, and system for image generation include obtaining a text embedding of a text prompt and an image embedding of an image prompt. Some embodiments map the text embedding into a joint embedding space to obtain a joint text embedding and map the image embedding into the joint embedding space to obtain a joint image embedding. Some embodiments generate a synthetic image based on the joint text embedding and the joint image embedding.

公开(公告)号：US20240249413A1

公开(公告)日：2024-07-25

申请号：US18100419

申请日：2023-01-23

Applicant: Adobe Inc.

Inventor： Jason Wen Yong Kuen ,  Zhe Lin ,  Sukjun Hwang ,  Jianming Zhang ,  Brian Lynn Price

IPC: G06T7/11

CPC classification number: G06T7/11 ,  G06T2207/20081 ,  G06T2207/20104

Abstract: In implementations of systems for performing multiple segmentation tasks, a computing device implements a segment system to receive input data describing a digital image depicting an object. The segment system computes per-pixel embeddings for the digital image using a pixel decoder of a machine learning model. Output embeddings are generated using a transformer decoder of the machine learning model based on the per-pixel embeddings for the digital image, input embeddings for a first segmentation task and input embeddings for a second segmentation task. The segment system outputs a first digital image and a second digital image. The first digital image depicts the object segmented based on the first segmentation task and the second digital image depicts the object segmented based on the second segmentation task.

公开(公告)号：US20240169630A1

公开(公告)日：2024-05-23

申请号：US18532457

申请日：2023-12-07

Applicant: Adobe Inc.

Inventor： Soo Ye Kim ,  Zhe Lin ,  Scott Cohen ,  Jianming Zhang ,  Luis Figueroa

IPC: G06T11/60 ,  G06F3/0481 ,  G06F3/04845 ,  G06F3/0486 ,  G06T5/00 ,  G06T11/00

CPC classification number: G06T11/60 ,  G06F3/0481 ,  G06F3/04845 ,  G06F3/0486 ,  G06T5/002 ,  G06T5/005 ,  G06T11/001 ,  G06T2200/24 ,  G06T2207/20092 ,  G06T2207/20212

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing to synthesize shadows for object(s). For instance, in one or more embodiments, the disclosed systems receive a digital image depicting a scene. The disclosed systems access an object mask of the object depicting in the digital image. The disclosed systems further combine the object mask, the digital image, and a noise representation to generate a combined representation. Moreover, the disclosed systems generate a shadow for the object from the combined representation and further generates the modified digital image by combining the shadow with the digital image.

公开(公告)号：US20240169541A1

公开(公告)日：2024-05-23

申请号：US18056987

申请日：2022-11-18

Applicant: ADOBE INC.

Inventor： Jianming Zhang ,  Qing Liu ,  Yilin Wang ,  Zhe Lin ,  Bowen Zhang

IPC: G06T7/10

CPC classification number: G06T7/10 ,  G06T2207/20081

Abstract: Systems and methods for instance segmentation are described. Embodiments include identifying an input image comprising an object that includes a visible region and an occluded region that is concealed in the input image. A mask network generates an instance mask for the input image that indicates the visible region of the object. A diffusion model then generates a segmentation mask for the input image based on the instance mask. The segmentation mask indicates a completed region of the object that includes the visible region and the occluded region.

公开(公告)号：US20240169501A1

公开(公告)日：2024-05-23

申请号：US18058601

申请日：2022-11-23

Applicant: Adobe Inc.

Inventor： Qing Liu ,  Zhe Lin ,  Luis Figueroa ,  Scott Cohen

IPC: G06T5/00 ,  G06T7/11 ,  G06T7/194

CPC classification number: G06T5/005 ,  G06T5/002 ,  G06T7/11 ,  G06T7/194 ,  G06T2200/24 ,  G06T2207/20084

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For instance, in one or more embodiments, the disclosed systems generate, utilizing a segmentation neural network and without user input, object masks for objects in a digital image. The disclosed systems determine foreground and background abutting an object mask. The disclosed systems generate an expanded object mask by expanding the object mask into the foreground abutting the object mask by a first amount and expanding the object mask into the background abutting the object mask by a second amount that differs from the first amount. The disclosed systems inpaint a hole corresponding to the expanded object mask utilizing an inpainting neural network.

公开(公告)号：US20240135512A1

公开(公告)日：2024-04-25

申请号：US18190556

申请日：2023-03-27

Applicant: Adobe Inc.

Inventor： Krishna Kumar Singh ,  Yijun Li ,  Jingwan Lu ,  Duygu Ceylan Aksit ,  Yangtuanfeng Wang ,  Jimei Yang ,  Tobias Hinz ,  Qing Liu ,  Jianming Zhang ,  Zhe Lin

IPC: G06T5/00 ,  G06T7/11 ,  G06V10/82 ,  G06V40/10

CPC classification number: G06T5/005 ,  G06T7/11 ,  G06V10/82 ,  G06V40/10 ,  G06T2207/20021 ,  G06T2207/20084 ,  G06T2207/20212 ,  G06T2207/30196

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via scene-based editing using image understanding facilitated by artificial intelligence. For example, in one or more embodiments the disclosed systems utilize generative machine learning models to create modified digital images portraying human subjects. In particular, the disclosed systems generate modified digital images by performing infill modifications to complete a digital image or human inpainting for portions of a digital image that portrays a human. Moreover, in some embodiments, the disclosed systems perform reposing of subjects portrayed within a digital image to generate modified digital images. In addition, the disclosed systems in some embodiments perform facial expression transfer and facial expression animations to generate modified digital images or animations.