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公开(公告)号：US20240037822A1
公开(公告)日：2024-02-01
申请号：US18228614
申请日：2023-07-31
Applicant: GOOGLE LLC
Inventor： Kfir Aberman , Amir Hertz , Yael Pritch Knaan , Ron Mokady , Jay Tenenbaum , Daniel Cohen-Or
IPC: G06T11/60 , G06F3/04845 , G06F40/40
CPC classification number: G06T11/60 , G06F3/04845 , G06F40/40
Abstract: Some implementations are directed to editing a source image, where the source image is one generated based on processing a source natural language (NL) prompt using a Large-scale language-image (LLI) model. Those implementations edit the source image based on user interface input that indicates an edit to the source NL prompt, and optionally independent of any user interface input that specifies a mask in the source image and/or independent of any other user interface input. Some implementations of the present disclosure are additionally or alternatively directed to applying prompt-to-prompt editing techniques to editing a source image that is one generated based on a real image, and that approximates the real image.
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公开(公告)号：US20230094723A1
公开(公告)日：2023-03-30
申请号：US17487741
申请日：2021-09-28
Applicant: Google LLC
Inventor： Kfir Aberman , Yael Pritch Knaan , David Edward Jacobs , Orly Liba
Abstract: Techniques for reducing a distractor object in a first image are presented herein. A system can access a mask and the first image. A distractor object in the first image can be inside a region of interest and can have a pixel with an original attribute. Additionally, the system can process, using a machine-learned inpainting model, the first image and the mask to generate an inpainted image. The pixel of the distractor object in the inpainted image can have an inpainted attribute in chromaticity channels. Moreover, the system can determine a palette transform based on a comparison of the first image and the inpainted image. The transform attribute can be different from the inpainted attribute. Furthermore, the system can process the first image to generate a recolorized image. The pixel in the recolorized image can have a recolorized attribute based on the transform attribute of the palette transform.
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公开(公告)号：US20240320912A1
公开(公告)日：2024-09-26
申请号：US18611236
申请日：2024-03-20
Applicant: Google LLC
Inventor： Yuanzhen Li , Amit Raj , Varun Jampani , Benjamin Joseph Mildenhall , Benjamin Michael Poole , Jonathan Tilton Barron , Kfir Aberman , Michael Niemeyer , Michael Rubinstein , Nataniel Ruiz Gutierrez , Shiran Elyahu Zada , Srinivas Kaza
IPC: G06T17/00 , H04N13/279 , H04N13/351
CPC classification number: G06T17/00 , H04N13/279 , H04N13/351
Abstract: A fractional training process can be performed training images to an instance of a machine-learned generative image model to obtain a partially trained instance of the model. A fractional optimization process can be performed with the partially trained instance to an instance of a machine-learned three-dimensional (3D) implicit representation model obtain a partially optimized instance of the model. Based on the plurality of training images, pseudo multi-view subject images can be generated with the partially optimized instance of the 3D implicit representation model and a fully trained instance of the generative image model; The partially trained instance of the model can be trained with a set of training data. The partially optimized instance of the machine-learned 3D implicit representation model can be trained with the machine-learned multi-view image model.
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公开(公告)号：US20230015117A1
公开(公告)日：2023-01-19
申请号：US17856370
申请日：2022-07-01
Applicant: Google LLC
Inventor： Kfir Aberman , David Edward Jacobs , Kai Jochen Kohlhoff , Michael Rubinstein , Yossi Gandelsman , Junfeng He , Inbar Mosseri , Yael Pritch Knaan
Abstract: Techniques for tuning an image editing operator for reducing a distractor in raw image data are presented herein. The image editing operator can access the raw image data and a mask. The mask can indicate a region of interest associated with the raw image data. The image editing operator can process the raw image data and the mask to generate processed image data. Additionally, a trained saliency model can process at least the processed image data within the region of interest to generate a saliency map that provides saliency values. Moreover, a saliency loss function can compare the saliency values provided by the saliency map for the processed image data within the region of interest to one or more target saliency values. Subsequently, the one or more parameter values of the image editing operator can be modified based at least in part on the saliency loss function.
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公开(公告)号：US20250069194A1
公开(公告)日：2025-02-27
申请号：US18946147
申请日：2024-11-13
Applicant: Google LLC
Inventor： Kfir Aberman , Yotam Nitzan , Orly Liba , Yael Pritch Knaan , Qiurui He , Inbar Mosseri , Yossi Gandelsman , Michal Yarom
Abstract: Systems and methods for identifying a personalized prior within a generative model's latent vector space based on a set of images of a given subject. In some examples, the present technology may further include using the personalized prior to confine the inputs of a generative model to a latent vector space associated with the given subject, such that when the model is tasked with editing an image of the subject (e.g., to perform inpainting to fill in masked areas, improve resolution, or deblur the image), the subject's identifying features will be reflected in the images the model produces.
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Patent Agency Ranking
公开(公告)号：US12169911B2
公开(公告)日：2024-12-17
申请号：US18334700
申请日：2023-06-14
Applicant: Google LLC
Inventor： Kfir Aberman , Yotam Nitzan , Orly Liba , Yael Pritch Knaan , Qiurui He , Inbar Mosseri , Yossi Gandelsman , Michal Yarom
Abstract: Systems and methods for identifying a personalized prior within a generative model's latent vector space based on a set of images of a given subject. In some examples, the present technology may further include using the personalized prior to confine the inputs of a generative model to a latent vector space associated with the given subject, such that when the model is tasked with editing an image of the subject (e.g., to perform inpainting to fill in masked areas, improve resolution, or deblur the image), the subject's identifying features will be reflected in the images the model produces.
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公开(公告)号：US20240296596A1
公开(公告)日：2024-09-05
申请号：US18569844
申请日：2023-08-23
Applicant: Google LLC
Inventor： Kfir Aberman , Nataniel Ruiz Gutierrez , Michael Rubinstein , Yuanzhen Li , Yael Pritch Knaan , Varun Jampani
IPC: G06T11/00 , G06V10/764
CPC classification number: G06T11/00 , G06V10/764 , G06V2201/07
Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a text-to-image model so that the text-to-image model generates images that each depict a variable instance of an object class when the object class without the unique identifier is provided as a text input, and that generates images that each depict a same subject instance of the object class when the unique identifier is provided as the text input.
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公开(公告)号：US20230325998A1
公开(公告)日：2023-10-12
申请号：US18334700
申请日：2023-06-14
Applicant: Google LLC
Inventor： Kfir Aberman , Yotam Nitzan , Orly Liba , Yael Pritch Knaan , Qiurui He , Inbar Mosseri , Yossi Gandelsman , Michal Yarom
CPC classification number: G06T5/50 , G06T5/001 , G06T3/40 , G06T2207/20081 , G06T2207/20084
Abstract: Systems and methods for identifying a personalized prior within a generative model's latent vector space based on a set of images of a given subject. In some examples, the present technology may further include using the personalized prior to confine the inputs of a generative model to a latent vector space associated with the given subject, such that when the model is tasked with editing an image of the subject (e.g., to perform inpainting to fill in masked areas, improve resolution, or deblur the image), the subject's identifying features will be reflected in the images the model produces.
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公开(公告)号：US20250037251A1
公开(公告)日：2025-01-30
申请号：US18717098
申请日：2022-01-13
Applicant: Google LLC
Inventor： Orly Liba , Kfir Aberman , Wei Xiong , David Futschik , Yael Pritch Knaan , Daniel Sýkora , Tianfan Xue
Abstract: A method includes obtaining an input image having a region to be inpainted, an indication of the region to be inpainted, and a guide image. The method also includes determining, by an encoder model, a first latent representation of the input image and a second latent representation of the guide image, and generating a combined latent representation based on the first latent representation and the second latent representation. The method additionally includes generating, by a style generative adversarial network model and based on the combined latent representation, an intermediate output image that includes inpainted image content for the region to be inpainted in the input image. The method further includes generating, based on the input image, the indication of the region, and the intermediate output image, an output image representing the input image with the region to be inpainted including the inpainted image content from the intermediate output image.
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公开(公告)号：US20240046532A1
公开(公告)日：2024-02-08
申请号：US18489539
申请日：2023-10-18
Applicant: Google LLC
Inventor： Kfir Aberman , Yael Pritch Knaan , Orly Liba , David Edward Jacobs
CPC classification number: G06T11/001 , G06N20/00 , G06T5/005 , G06T11/60
Abstract: Techniques for reducing a distractor object in a first image are presented herein. A system can access a mask and the first image. A distractor object in the first image can be inside a region of interest and can have a pixel with an original attribute. Additionally, the system can process, using a machine-learned inpainting model, the first image and the mask to generate an inpainted image. The pixel of the distractor object in the inpainted image can have an inpainted attribute in chromaticity channels. Moreover, the system can determine a palette transform based on a comparison of the first image and the inpainted image. The transform attribute can be different from the inpainted attribute. Furthermore, the system can process the first image to generate a recolorized image. The pixel in the recolorized image can have a recolorized attribute based on the transform attribute of the palette transform.
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