Patents Assigned to Adobe Inc.
  • Publication number: 20250111610
    Abstract: A computing system receives a query for a three-dimensional representation of a target object. The query comprises input in the form of text describing the target object, a two-dimensional image of the target object, or a three-dimensional model of the target object. The computing system encodes the input using a machine learning model to generate an encoded representation of the input. The computing system searches a search space using nearest neighbors to identify a three-dimensional representation of the target object. The search space comprises encoded representations of multiple views of a plurality of sample three-dimensional object representations. The computing system outputs the identified three-dimensional representation of the target object.
    Type: Application
    Filed: September 28, 2023
    Publication date: April 3, 2025
    Applicant: Adobe Inc.
    Inventors: Thibault Groueix, Vladimir Kim
  • Publication number: 20250111866
    Abstract: Embodiments are disclosed for editing video using image diffusion. The method may include receiving an input video depicting a target and a prompt including an edit to be made to the target. A keyframe associated with the input video is then identified. The keyframe is edited, using a generative neural network, based on the prompt to generate an edited keyframe. A subsequent frame of the input video is edited using the generative neural network, based on the prompt, features of the edited keyframe, and features of an intervening frame to generate an edited output video.
    Type: Application
    Filed: October 2, 2023
    Publication date: April 3, 2025
    Applicant: Adobe Inc.
    Inventors: Duygu Ceylan Aksit, Niloy Mitra, Chun-Hao Huang
  • Publication number: 20250111566
    Abstract: In implementations of systems and procedures for generating surrogate curvatures for assisted vector drawings, a computing device implements acquisition of a target vector curve and compares a curvature of the target vector curve to a curvature of a reference vector curve. The computing device determines whether the curvature of the target vector curve is within a threshold tolerance of the curvature of the reference vector curve. An edited curvature of the targeted vector curve is generated based on the curvature of the reference vector curve.
    Type: Application
    Filed: October 2, 2023
    Publication date: April 3, 2025
    Applicant: Adobe Inc.
    Inventors: Vivek Agrawal, Tarun Beri
  • Publication number: 20250111695
    Abstract: In implementation of techniques for template-based behaviors in machine learning, a computing device implements a template system to receive a digital video and data executable to generate animated content. The template system determines a location within a frame of the digital video to place the animated content using a machine learning model. The template system then renders the animated content within the frame of the digital video at the location determined by the machine learning model. The template system then displays the rendered animated content within the frame of the digital video in a user interface.
    Type: Application
    Filed: December 18, 2023
    Publication date: April 3, 2025
    Applicant: Adobe Inc.
    Inventors: Wilmot Wei-Mau Li, Li-Yi Wei, Cuong D. Nguyen, Jakub Fiser, Hijung Shin, Stephen Joseph DiVerdi, Seth John Walker, Kazi Rubaiat Habib, Deepali Aneja, David Gilliaert Werner, Erica K. Schisler
  • Patent number: 12266181
    Abstract: Embodiments are disclosed for receiving a user input and an input video comprising multiple frames. The method may include extracting a text feature from the user input. The method may further include extracting a plurality of image features from the frames. The method may further include identifying one or more keyframes from the frames that include the object. The method may further include clustering one or more groups of the one or more keyframes. The method may further include generating a plurality of segmentation masks for each group. The method may further include determining a set of reference masks corresponding to the user input and the object. The method may further include generating a set of fusion masks by combining the plurality of segmentation masks and the set of reference masks. The method may further include propagating the set of fusion masks and outputting a final set of masks.
    Type: Grant
    Filed: November 19, 2021
    Date of Patent: April 1, 2025
    Assignee: Adobe Inc.
    Inventors: Shivam Nalin Patel, Kshitiz Garg, Han Guo, Ali Aminian, Aashish Misraa
  • Patent number: 12266076
    Abstract: The present disclosure relates to systems, methods, and non-transitory computer-readable media that generates a design representation to further construct a digital design multigraph and generate a structural representation for a digital design document from the digital design multigraph. For instance, the disclosed systems generate a design representation of a digital design document that includes design properties with multiple digital design elements. In particular, the disclosed systems construct a digital design (multi-)graph from the design representation by generating nodes to represent digital design elements and edges based on relationships between these elements. In addition, the disclosed systems generate a structural representation based on the digital design multigraph for downstream applications.
    Type: Grant
    Filed: June 2, 2023
    Date of Patent: April 1, 2025
    Assignee: Adobe Inc.
    Inventors: David Bourgin, Peter O'Donovan, Oliver Brdiczka, Gregory St. Pierre, Abhishek Gulati
  • Patent number: 12266039
    Abstract: Certain aspects and features of this disclosure relate to rendering images using target-augmented material maps. In one example, a graphics imaging application is loaded with a scene and an input material map, as well as a file for a target image. A stored, material generation prior is accessed by the graphics imaging application. This prior, as an example, is based on a pre-trained, generative adversarial network (GAN). An input material appearance from the input material map is encoded to produce a projected latent vector. The value for the projected latent vector is optimized to produce the material map that is used to render the scene, producing a material map augmented by a realistic target material appearance.
    Type: Grant
    Filed: November 11, 2022
    Date of Patent: April 1, 2025
    Assignee: Adobe Inc.
    Inventors: Valentin Deschaintre, Yiwei Hu, Paul Guerrero, Milos Hasan
  • Patent number: 12265557
    Abstract: Graphic visualizations, such as charts or graphs conveying data attribute values, can be generated based on natural language queries, i.e., natural language requests. To do so, a natural language request is parsed into n-grams, and from the n-grams, word embeddings are determined using a natural language model. Data attributes for the graphic visualization are discovered in the vector space from the word embeddings. The type of graphic visualization can be determined based on a request intent, which is determined using a trained intent classifier. The graphic visualization is generated to include the data attribute values of the discovered data attributes, and in accordance with the graphic visualization type.
    Type: Grant
    Filed: August 31, 2023
    Date of Patent: April 1, 2025
    Assignee: Adobe Inc.
    Inventors: William Brandon George, Wei Zhang, Tyler Rasmussen, Tung Mai, Tong Yu, Sungchul Kim, Shunan Guo, Samuel Nephi Grigg, Said Kobeissi, Ryan Rossi, Ritwik Sinha, Eunyee Koh, Prithvi Bhutani, Jordan Henson Walker, Abhisek Trivedi
  • Patent number: 12265652
    Abstract: A method includes populating a template database with templates associated with template identifiers (IDs) identifying the templates. The method also includes generating a data model that references a template within the template database, where the data model includes a template ID referencing the template in the template database, and where the template includes a parameter field. The data model further includes a template parameter to apply to the parameter field and a digital signature for at least the template ID and the template parameter. The method also includes deploying the data model within a distributed ledger.
    Type: Grant
    Filed: November 15, 2022
    Date of Patent: April 1, 2025
    Assignee: Adobe Inc.
    Inventors: Songlin He, Tong Sun, Rajiv Jain, Nedim Lipka, Curtis Wigington, Anindo Roy
  • Patent number: 12265792
    Abstract: Methods and systems are provided for facilitating generation and utilization of a commonsense contextualizing machine learning (ML) model, in accordance with embodiments described herein. In embodiments, a commonsense contextual ML model is trained by fine-tuning a pre-trained language model using a set of training path-sentence pairs. Each training path-sentence pair includes a commonsense path, identified via a commonsense knowledge graph, and a natural language sentence identified as contextually related to the commonsense path. The trained commonsense contextualizing ML model can then be used to generate a commonsense inference path for a text input. Such a commonsense inference path can include a sequence of entities and relations that provide commonsense context to the text input. Thereafter, the commonsense inference path can be provided to a natural language processing system for use in performing a natural language processing task.
    Type: Grant
    Filed: November 15, 2021
    Date of Patent: April 1, 2025
    Assignee: Adobe Inc.
    Inventors: Rachit Bansal, Milan Aggarwal, Sumit Bhatia, Jivat Neet Kaur, Balaji Krishnamurthy
  • Patent number: 12266112
    Abstract: Embodiments are disclosed for generating an instant mask from polarized input images.
    Type: Grant
    Filed: November 29, 2021
    Date of Patent: April 1, 2025
    Assignee: Adobe Inc.
    Inventors: Tenell Rhodes, Brian Price, Gavin Stuart Peter Miller, Kenji Enomoto
  • Patent number: 12267305
    Abstract: Systems and techniques for privacy preserving document analysis are described that derive insights pertaining to a digital document without communication of the content of the digital document. To do so, the privacy preserving document analysis techniques described herein capture visual or contextual features of the digital document and creates a stamp representation that represents these features without included the content of the digital document. The stamp representation is projected into a stamp embedding space based on a stamp encoding model generated through machine learning techniques capturing feature patterns and interaction in the stamp representations. The stamp encoding model exploits these feature interactions to define similarity of source documents based on location within the stamp embedding space. Accordingly, the techniques described herein can determine a similarity of documents without having access to the documents themselves.
    Type: Grant
    Filed: May 15, 2023
    Date of Patent: April 1, 2025
    Assignee: Adobe Inc.
    Inventors: Nikolaos Barmpalios, Ruchi Rajiv Deshpande, Randy Lee Swineford, Nargol Rezvani, Andrew Marc Greene, Shawn Alan Gaither, Michael Kraley
  • Publication number: 20250103649
    Abstract: Embodiments are disclosed for performing content authentication. A method of content authentication may include dividing a query video into a plurality of chunks. A feature vector may be generated, using a fingerprinting model, for each chunk from the plurality of chunks. Similar video chunks are identified from a trusted chunk database based on the feature vectors using a multi-chunk search policy. One or more original videos corresponding to the query video are then returned.
    Type: Application
    Filed: September 22, 2023
    Publication date: March 27, 2025
    Applicant: Adobe Inc.
    Inventors: Ritwik SINHA, Viswanathan SWAMINATHAN, Simon JENNI, Md Mehrab TANJIM, John COLLOMOSSE
  • Publication number: 20250104305
    Abstract: Systems and methods are disclosed for reflowing documents to display semantically related content. Embodiments may include receiving a request to view a document that includes body text and one or more images. A trimodal document relationship model identifies relationships between segments of the body text and the one or more images. A linearized view of the document is generated based on the relationships and the linearized view is caused to be displayed on a user device.
    Type: Application
    Filed: September 25, 2023
    Publication date: March 27, 2025
    Applicant: Adobe Inc.
    Inventors: Sanjeev Tagra, Sachin Soni, Prasenjit Mondal, Ajay Jain
  • Publication number: 20250104288
    Abstract: Techniques for latent space based steganographic image generation are described. A processing device, for instance, receives a digital image and a secret that includes a bit string. A pretrained encoder of an autoencoder generates an embedding of the digital image that includes latent code. A secret encoder is trained and utilized to generate an embedding of the secret to act as a latent offset to the latent code. The processing device leverages a pretrained decoder of the autoencoder to generate a steganographic image based on the embedding of the secret and the embedding of the digital image. The steganographic image includes the secret and is visually indiscernible from the digital image. Further, the processing device is configured to recover the secret from the steganographic image, such as by training and leveraging a secret decoder to extract the secret.
    Type: Application
    Filed: September 21, 2023
    Publication date: March 27, 2025
    Applicant: Adobe Inc.
    Inventors: Shruti Agarwal, John Philip Collomosse
  • Patent number: 12260475
    Abstract: Embodiments are disclosed for performing content linting in a graphic design system. A method of content linting includes receiving a selection of a content type to be generated, receiving a selection of a location in a digital canvas to place content of the content type, determining a placement context associated with the location in the digital canvas, identifying one or more content rules to the content based on a static analysis of the placement context, and generating, using one or more machine learning models, content of the selected content type at the location in the digital canvas using the one or more content rules.
    Type: Grant
    Filed: January 27, 2022
    Date of Patent: March 25, 2025
    Assignee: Adobe Inc.
    Inventors: Gregory Cy Muscolino, Christian Cantrell, Archie Samuel Bagnall, Christopher James Gammon, Patrick James Hebron
  • Patent number: 12260530
    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.
    Type: Grant
    Filed: March 27, 2023
    Date of Patent: March 25, 2025
    Assignee: Adobe Inc.
    Inventors: Krishna Kumar Singh, Yijun Li, Jingwan Lu, Duygu Ceylan Aksit, Yangtuanfeng Wang, Jimei Yang, Tobias Hinz, Qing Liu, Jianming Zhang, Zhe Lin
  • Patent number: 12260557
    Abstract: An image processing system generates an image mask from an image. The image is processed by an object detector to identify a region having an object, and the region is classified based on an object type of the object. A masking pipeline is selected from a number of masking pipelines based on the classification of the region. The region is processed using the masking pipeline to generate a region mask. An image mask for the image is generated using the region mask.
    Type: Grant
    Filed: June 13, 2022
    Date of Patent: March 25, 2025
    Assignee: adobe inc.
    Inventors: Zijun Wei, Yilin Wang, Jianming Zhang, He Zhang
  • Patent number: 12260480
    Abstract: Embodiments are disclosed for machine learning-based generation of recommended layouts. The method includes receiving a set of design elements for performing generative layout recommendation. A number of each type of design element from the set of design elements is determined. A set of recommended layouts are generated using a trained generative layout model and the number and type of design elements. The set of recommended layouts are output.
    Type: Grant
    Filed: March 6, 2023
    Date of Patent: March 25, 2025
    Assignee: Adobe Inc.
    Inventors: Sukriti Verma, Venkata naveen kumar Yadav Marri, Ritiz Tambi, Pranav Vineet Aggarwal, Peter O'Donovan, Midhun Harikumar, Ajinkya Kale
  • Publication number: 20250095631
    Abstract: Position-based text-to-speech model and training techniques are described. A digital document, for instance, is received by an audio synthesis service. A text-to-speech model is utilized by the audio synthesis service to generate digital audio from text included in the digital document. The text-to-speech model, for instance, is configured to generate a text encoding and a document positional encoding from an initial text sequence of the digital document. The document positional encoding is based on a location of the text encoding within the digital document. Digital audio is then generated by the text-to-speech model that includes a spectrogram having a reordered text sequence, which is different from the initial text sequence, by decoding the text encoding and the document positional encoding.
    Type: Application
    Filed: December 4, 2023
    Publication date: March 20, 2025
    Applicant: Adobe Inc.
    Inventors: Puneet Mathur, Franck Dernoncourt, Quan Hung Tran, Jiuxiang Gu, Ani Nenkova, Vlad Ion Morariu, Rajiv Bhawanji Jain, Dinesh Manocha