公开(公告)号：US20240046412A1

公开(公告)日：2024-02-08

申请号：US17880120

申请日：2022-08-03

Applicant: ADOBE INC.

Inventor： Md Mehrab Tanjim ,  Krishna Kumar Singh ,  Kushal Kafle ,  Ritwik Sinha

IPC: G06T3/40

CPC classification number: G06T3/4046 ,  G06T3/4053

Abstract: A system debiases image translation models to produce generated images that contain minority attributes. A balanced batch for a minority attribute is created by over-sampling images having the minority attribute from an image dataset. An image translation model is trained using images from the balanced batch by applying supervised contrastive loss to output of an encoder of the image translation model and an auxiliary classifier loss based on predicted attributes in images generated by a decoder of the image translation model. Once trained, the image translation model is used to generate images with the minority image when given an input image having the minority attribute.

公开(公告)号：US11769227B2

公开(公告)日：2023-09-26

申请号：US17400426

申请日：2021-08-12

Applicant: Adobe Inc.

Inventor： Yuheng Li ,  Yijun Li ,  Jingwan Lu ,  Elya Shechtman ,  Krishna Kumar Singh

IPC: G06K9/00 ,  G06T3/40 ,  G06N3/04 ,  G06V10/40 ,  G06V30/262 ,  G06F18/25

CPC classification number: G06T3/4046 ,  G06F18/253 ,  G06N3/04 ,  G06V10/40 ,  G06V30/274

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that generate synthetized digital images via multi-resolution generator neural networks. The disclosed system extracts multi-resolution features from a scene representation to condition a spatial feature tensor and a latent code to modulate an output of a generator neural network. For example, the disclosed systems utilizes a base encoder of the generator neural network to generate a feature set from a semantic label map of a scene. The disclosed system then utilizes a bottom-up encoder to extract multi-resolution features and generate a latent code from the feature set. Furthermore, the disclosed system determines a spatial feature tensor by utilizing a top-down encoder to up-sample and aggregate the multi-resolution features. The disclosed system then utilizes a decoder to generate a synthesized digital image based on the spatial feature tensor and the latent code.

公开(公告)号：US20240338869A1

公开(公告)日：2024-10-10

申请号：US18474536

申请日：2023-09-26

Applicant: ADOBE INC.

Inventor： Yuqian Zhou ,  Krishna Kumar Singh ,  Zhifei Zhang ,  Difan Liu ,  Zhe Lin ,  Jianming Zhang ,  Qing Liu ,  Jingwan Lu ,  Elya Shechtman ,  Sohrab Amirghodsi ,  Connelly Stuart Barnes

IPC: G06T11/60

CPC classification number: G06T11/60

Abstract: An image processing system obtains an input image (e.g., a user provided image, etc.) and a mask indicating an edit region of the image. A user selects an image editing mode for an image generation network from a plurality of image editing modes. The image generation network generates an output image using the input image, the mask, and the image editing mode.

公开(公告)号：US20240265505A1

公开(公告)日：2024-08-08

申请号：US18165141

申请日：2023-02-06

Applicant: ADOBE INC.

Inventor： Cusuh Ham ,  Tobias Hinz ,  Jingwan Lu ,  Krishna Kumar Singh ,  Zhifei Zhang

IPC: G06T5/00

CPC classification number: G06T5/70 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Systems and methods for image processing are described. Embodiments of the present disclosure obtain a noise image and guidance information for generating an image. A diffusion model generates an intermediate noise prediction for the image based on the noise image. A conditioning network generates noise modulation parameters. The intermediate noise prediction and the noise modulation parameters are combined to obtain a modified intermediate noise prediction. The diffusion model generates the image based on the modified intermediate noise prediction, wherein the image depicts a scene based on the guidance information.

公开(公告)号：US20240233318A9

公开(公告)日：2024-07-11

申请号：US17971169

申请日：2022-10-21

Applicant: Adobe Inc.

Inventor： Yijun Li ,  Zhixin Shu ,  Zhen Zhu ,  Krishna Kumar Singh

IPC: G06V10/70 ,  G06N3/04 ,  G06T11/00 ,  G06T15/08

CPC classification number: G06V10/70 ,  G06N3/0454 ,  G06T11/001 ,  G06T15/08

Abstract: An image generation system implements a multi-branch GAN to generate images that each express visually similar content in a different modality. A generator portion of the multi-branch GAN includes multiple branches that are each tasked with generating one of the different modalities. A discriminator portion of the multi-branch GAN includes multiple fidelity discriminators, one for each of the generator branches, and a consistency discriminator, which constrains the outputs generated by the different generator branches to appear visually similar to one another. During training, outputs from each of the fidelity discriminators and the consistency discriminator are used to compute a non-saturating GAN loss. The non-saturating GAN loss is used to refine parameters of the multi-branch GAN during training until model convergence. The trained multi-branch GAN generates multiple images from a single input, where each of the multiple images depicts visually similar content expressed in a different modality.

公开(公告)号：US12001520B2

公开(公告)日：2024-06-04

申请号：US17485780

申请日：2021-09-27

Applicant: Adobe Inc.

Inventor： Ritwik Sinha ,  Sridhar Mahadevan ,  Moumita Sinha ,  Md Mehrab Tanjim ,  Krishna Kumar Singh ,  David Arbour

IPC: G06K9/00 ,  G06F18/214 ,  G06F18/28 ,  G06N3/045

CPC classification number: G06F18/28 ,  G06F18/2148 ,  G06N3/045

Abstract: Methods and systems disclosed herein relate generally to systems and methods for generating simulated images for enhancing socio-demographic diversity. An image-generating application receives a request that includes a set of target socio-demographic attributes. The set of target socio-demographic attributes can define a gender, age, and/or race of a subject that are non-stereotypical for a particular occupation. The image-generating application applies the a machine-learning model to the set of target socio-demographic attributes. The machine-learning model generates a simulated image depicts a subject having visual characteristics that are defined by the set of target socio-demographic attributes.

公开(公告)号：US20240144623A1

公开(公告)日：2024-05-02

申请号：US18304147

申请日：2023-04-20

Applicant: Adobe Inc.

Inventor： Giorgio Gori ,  Yi Zhou ,  Yangtuanfeng Wang ,  Yang Zhou ,  Krishna Kumar Singh ,  Jae Shin Yoon ,  Duygu Ceylan Aksit

IPC: G06T19/20 ,  G06T7/70 ,  G06T15/00 ,  G06T17/00

CPC classification number: G06T19/20 ,  G06T7/70 ,  G06T15/00 ,  G06T17/00 ,  G06T2200/24 ,  G06T2207/20084 ,  G06T2207/30196 ,  G06T2207/30244 ,  G06T2219/2004

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify two-dimensional images via scene-based editing using three-dimensional representations of the two-dimensional images. For instance, in one or more embodiments, the disclosed systems utilize three-dimensional representations of two-dimensional images to generate and modify shadows in the two-dimensional images according to various shadow maps. Additionally, the disclosed systems utilize three-dimensional representations of two-dimensional images to modify humans in the two-dimensional images. The disclosed systems also utilize three-dimensional representations of two-dimensional images to provide scene scale estimation via scale fields of the two-dimensional images. In some embodiments, the disclosed systems utilizes three-dimensional representations of two-dimensional images to generate and visualize 3D planar surfaces for modifying objects in two-dimensional images. The disclosed systems further use three-dimensional representations of two-dimensional images to customize focal points for the two-dimensional images.

公开(公告)号：US20240135572A1

公开(公告)日：2024-04-25

申请号：US18190636

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

IPC: G06T7/70 ,  G06T7/40 ,  G06V10/44 ,  G06V10/771 ,  G06V10/80 ,  G06V10/82

CPC classification number: G06T7/70 ,  G06T7/40 ,  G06V10/44 ,  G06V10/771 ,  G06V10/806 ,  G06V10/82 ,  G06T2207/20081 ,  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.

公开(公告)号：US20240135514A1

公开(公告)日：2024-04-25

申请号：US18460365

申请日：2023-09-01

Applicant: Adobe Inc.

Inventor： Daniil Pakhomov ,  Qing Liu ,  Zhihong Ding ,  Scott Cohen ,  Zhe Lin ,  Jianming Zhang ,  Zhifei Zhang ,  Ohiremen Dibua ,  Mariette Souppe ,  Krishna Kumar Singh ,  Jonathan Brandt

IPC: G06T5/00 ,  G06F3/04845 ,  G06T7/11 ,  G06T7/194 ,  G06T7/70

CPC classification number: G06T5/005 ,  G06F3/04845 ,  G06T5/002 ,  G06T7/11 ,  G06T7/194 ,  G06T7/70 ,  G06T2200/24 ,  G06T2207/20021 ,  G06T2207/20084 ,  G06T2207/20092

Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that modify digital images via multi-layered scene completion techniques facilitated by artificial intelligence. For instance, in some embodiments, the disclosed systems receive a digital image portraying a first object and a second object against a background, where the first object occludes a portion of the second object. Additionally, the disclosed systems pre-process the digital image to generate a first content fill for the portion of the second object occluded by the first object and a second content fill for a portion of the background occluded by the second object. After pre-processing, the disclosed systems detect one or more user interactions to move or delete the first object from the digital image. The disclosed systems further modify the digital image by moving or deleting the first object and exposing the first content fill for the portion of the second object.

公开(公告)号：US11842468B2

公开(公告)日：2023-12-12

申请号：US17178681

申请日：2021-02-18

Applicant: Adobe Inc.

Inventor： Pei Wang ,  Yijun Li ,  Jingwan Lu ,  Krishna Kumar Singh

IPC: G06K9/00 ,  G06T5/50 ,  G06N3/04 ,  G06V10/75 ,  G06F18/22 ,  G06F18/24

CPC classification number: G06T5/50 ,  G06F18/22 ,  G06F18/24 ,  G06N3/04 ,  G06V10/751 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20221 ,  G06V10/759

Abstract: This disclosure describes methods, non-transitory computer readable storage media, and systems that utilize image-guided model inversion of an image classifier with a discriminator. The disclosed systems utilize a neural network image classifier to encode features of an initial image and a target image. The disclosed system also reduces a feature distance between the features of the initial image and the features of the target image at a plurality of layers of the neural network image classifier by utilizing a feature distance regularizer. Additionally, the disclosed system reduces a patch difference between image patches of the initial image and image patches of the target image by utilizing a patch-based discriminator with a patch consistency regularizer. The disclosed system then generates a synthesized digital image based on the constrained feature set and constrained image patches of the initial image.