公开(公告)号：US20250166355A1

公开(公告)日：2025-05-22

申请号：US18517407

申请日：2023-11-22

Applicant: Adobe Inc.

Inventor： Arthur Jules Martin Roullier ,  Tamy Boubekeur ,  Rosalie Noémie Raphaëlle Martin ,  Romain Pierre Rouffet ,  Adrien Michel Paul Kaiser

IPC: G06V10/77 ,  G06V10/764 ,  G06V10/82 ,  G06V20/70

Abstract: In implementation of techniques for translating images based on semantic information, a computing device implements a translation system to receive an input image in a first format, encoded semantic information describing a domain of the input image, and a selection of a second format. The translation system decodes the encoded semantic information using a machine learning model. The translation system then generates an output image in the second format by translating the input image from the first format to the second format using the machine learning model, the machine learning model guided by the decoded semantic information. The translation system then displays the output image in the second format in a user interface.

公开(公告)号：US20250147973A1

公开(公告)日：2025-05-08

申请号：US18504256

申请日：2023-11-08

Applicant: ADOBE INC.

Inventor： Tong Yu ,  Xiang Chen ,  Victor Soares Bursztyn ,  Uttaran Bhattacharya ,  Eunyee Koh ,  Saayan Mitra ,  Alexandru Ionut Hodorogea ,  Kenneth Russell ,  Saurabh Tripathy ,  Manas Garg

IPC: G06F16/2457 ,  G06F16/93 ,  G06N20/20

Abstract: A method, apparatus, non-transitory computer readable medium, and system for document retrieval include obtaining a query and a document. A prompt generator generates a prompt for a reasoning model based on the query and the document. The reasoning model generates a reasoning result based on the prompt. In some cases, the reasoning result indicates that the document answers the query. A machine learning model provides the document in response to the query based on the reasoning result.

公开(公告)号：US12294529B2

公开(公告)日：2025-05-06

申请号：US18342516

申请日：2023-06-27

Applicant: ADOBE INC.

Inventor： Kanak Mahadik ,  Tong Yu ,  Junda Wu

IPC: H04L41/16 ,  H04L47/78

Abstract: Methods for determining optimal cloud service resource include determining a reward function for a set of resource configurations identifying cloud service resource parameters. The cloud service resource parameters include a source parameter and a target parameter of services to provide a client computing device. A source parameter dataset for the source parameter and a target parameter dataset is generated using the reward function and historical source parameter data. The matrices are then subject to SVD and clustering. A target parameter reward dataset is learned from output of the SVD and clustering. The target parameter dataset is used to determine the parameters for the target parameter for providing corresponding cloud service resources.

公开(公告)号：US12293577B2

公开(公告)日：2025-05-06

申请号：US17651771

申请日：2022-02-18

Applicant: Adobe Inc.

Inventor： Seunghyun Yoon ,  Trung Huu Bui ,  Franck Dernoncourt ,  Hyounghun Kim ,  Doo Soon Kim

IPC: G06V10/86 ,  G06F40/284 ,  G06N3/044 ,  G06N3/088 ,  G06V10/77 ,  G06V10/80 ,  G06V10/82

Abstract: Embodiments of the disclosure provide a machine learning model for generating a predicted executable command for an image. The learning model includes an interface configured to obtain an utterance indicating a request associated with the image, an utterance sub-model, a visual sub-model, an attention network, and a selection gate. The machine learning model generates a segment of the predicted executable command from weighted probabilities of each candidate token in a predetermined vocabulary determined based on the visual features, the concept features, current command features, and the utterance features extracted from the utterance or the image.

公开(公告)号：US20250142182A1

公开(公告)日：2025-05-01

申请号：US18584210

申请日：2024-02-22

Applicant: ADOBE INC.

Inventor： Joanna Irena Materzynska ,  Richard Zhang ,  Elya Shechtman ,  Josef Sivic ,  Bryan Christopher Russell

IPC: H04N21/81 ,  H04N21/488

Abstract: Systems and methods include generating synthetic videos based on a custom motion. A video generation system obtains a text prompt including an object and a custom motion token. The custom motion token represents a custom motion. The system encodes the text prompt to obtain a text embedding. Subsequently, a video generation model generates a synthetic video depicting the object performing the custom motion based on the text embedding using a video generation model.

公开(公告)号：US20250139883A1

公开(公告)日：2025-05-01

申请号：US18499673

申请日：2023-11-01

Applicant: ADOBE INC.

Inventor： Milos Hasan ,  Iliyan Georgiev ,  Sai Bi ,  Julien Philip ,  Kalyan K. Sunkavalli ,  Xin Sun ,  Fujun Luan ,  Kevin James Blackburn-Matzen ,  Zexiang Xu ,  Kai Zhang

IPC: G06T17/00 ,  G06T7/90 ,  H04N13/279

Abstract: Embodiments are configured to render 3D models using an importance sampling method. First, embodiments obtain a 3D model including a plurality of density values corresponding to a plurality of locations in a 3D space, respectively. Embodiments then sample the color information from within a random subset of the plurality of locations using a probability distribution based on the plurality of density values. Embodiments have a higher probability to sample each location within the random subset of locations if the location has a higher density probability. Embodiments then an image depicting a view of the 3D model based on the sampling within the random subset of the plurality of locations.

公开(公告)号：US12288279B2

公开(公告)日：2025-04-29

申请号：US18058538

申请日：2022-11-23

Applicant: Adobe Inc.

Inventor： Jonathan Brandt ,  Scott Cohen ,  Zhe Lin ,  Zhihong Ding ,  Darshan Prasad ,  Matthew Joss ,  Celso Gomes ,  Jianming Zhang ,  Olena Soroka ,  Klaas Stoeckmann ,  Michael Zimmermann ,  Thomas Muehrke

IPC: G06T11/60 ,  G06F3/048 ,  G06F3/04842 ,  G06F3/04845 ,  G06T11/40

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, an object mask for each object of a plurality of objects of a digital image. The disclosed systems detect a first user interaction with an object in the digital image displayed via a graphical user interface. The disclosed systems surface, via the graphical user interface, the object mask for the object in response to the first user interaction. The disclosed systems perform an object-aware modification of the digital image in response to a second user interaction with the object mask for the object.

公开(公告)号：US20250131604A1

公开(公告)日：2025-04-24

申请号：US18491472

申请日：2023-10-20

Applicant: ADOBE INC.

Inventor： Adrian-Stefan Ungureanu ,  Vlad-Constantin Lungu-Stan ,  Ionut Mironicã

IPC: G06T11/00 ,  G06F3/04845 ,  G06F3/04847

Abstract: Embodiments include obtaining a prompt and a diversity input indicating a level of adherence to the prompt. The diversity input may be implemented as a graphical user interface (GUI) element, such as a slider or field. Embodiments then generate a guidance embedding based on the prompt and the diversity input. Embodiments update the guidance embedding based on the diversity input. Subsequently, embodiments generate a synthetic image based on the guidance embedding, wherein the synthetic image depicts an element of the prompt based on the level of adherence from the diversity input.

公开(公告)号：US20250124235A1

公开(公告)日：2025-04-17

申请号：US18485204

申请日：2023-10-11

Applicant: ADOBE INC.

Inventor： Victor Soares BURSZTYN ,  Xiang CHEN ,  Vaishnavi MUPPALA ,  Uttaran BHATTACHARYA ,  Tong YU ,  Saayan MITRA ,  Ryan ROSSI ,  Manas GARG ,  Kenneth George RUSSELL ,  Eunyee KOH ,  Alexandru Ionut HODOROGEA

IPC: G06F40/40 ,  G06F40/279

Abstract: Methods and systems are provided for using generative artificial intelligence to evaluate fine-tuned language models. In embodiments described herein, natural language text snippets are generated via a generative language model based on corresponding data. A language model is fine-tuned into a fine-tuned language model via a language model fine-tuning component using the natural language text snippets and the corresponding data as training data. Independent natural language text snippets are generated via the generative language model based on the corresponding data. Each independent natural language text snippet is different than each corresponding natural language text snippet. An evaluation metric of the fine-tuned language model is generated via an evaluation component based on the independent natural language text snippets and the corresponding data.

公开(公告)号：US20250124212A1

公开(公告)日：2025-04-17

申请号：US18507847

申请日：2023-11-13

Applicant: Adobe Inc.

Inventor： Difan Liu ,  Matthew David Fisher ,  Michaël Yanis Gharbi ,  Oliver Wang ,  Alec Stefan Jacobson ,  Vikas Thamizharasan ,  Evangelos Kalogerakis

IPC: G06F40/109 ,  G06T11/20 ,  G06T11/40

Abstract: In implementation of techniques for vector font generation based on cascaded diffusion, a computing device implements a glyph generation system to receive a sample glyph in a target font and a target glyph identifier. The glyph generation system generates a rasterized glyph in the target font using a raster diffusion model based on the sample glyph and the target glyph identifier, the rasterized glyph having a first level of resolution. The glyph generation system then generates a vector glyph using a vector diffusion model by vectorizing the rasterized glyph, the vector glyph having a second level of resolution different than the first level of resolution. The glyph generation system then displays the vector glyph in a user interface.