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.

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.

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.

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.

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.

Abstract: Embodiments are disclosed for generating an instant mask from polarized input images.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Abstract: Embodiments are disclosed for recoloring a target graphic using color palettes generated using a stochastic color mapping process. One method of recoloring a target graphic using the stochastic color mapping process includes obtaining a target graphic to be recolored and a source color palette defining source colors for recoloring the target graphic. A target color set of target colors is extracted from the target graphic. The method includes computing a mapping to map source colors of a source color palette to target colors extracted from a target color set of the target graphic based on a transition probability. A destination color palette of destination colors is determined based on the mapping. The target graphic is modified by recoloring at least one object in the target graphic with a destination color from the destination color palette.

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.

Abstract: In accordance with the described techniques, a background generation system receives one or more images depicting an object, and textual information describing the object. A generative text model is employed to generate a prompt based on the one or more images and the textual information. Further, a generative image model is employed to generate an output image. To do so, the generative image model generates a background image based on the prompt, and the object is incorporated into the background image. Using a visual saliency model, the background generation system determines a visual saliency defining a degree of fixation on the object within the output image. The background generation system outputs the output image based on the visual saliency meeting a threshold.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for performing object-specific undo and/or redo operations. For example, in one or more embodiments, the disclosed systems receive a modified digital design image comprising a first modified object and a second modified object. In some examples, the second modified object is modified after the first modified object. The disclosed systems can generate and utilize an object-specific version representation to undo an edit to the first modified object without undoing edits to the second modified object. The disclosed systems can generate and provide, for display via a user interface, an updated digital design document comprising a reverted first object and the second modified object.

Abstract: An item recommendation system receives a set of recommendable items and a request to select, from the set of recommendable items, a contrast group. The item recommendation system selects a contrast group from the set of recommendable items by applying a image modification model to the set of recommendable items. The image modification model includes an item selection model configured to determine an unbiased conversion rate for each item of the set of recommendable items and select a recommended item from the set of recommendable items having a greatest unbiased conversion rate. The image modification model includes a contrast group selection model configured to select, for the recommended item, a contrast group comprising the recommended item and one or more contrast items. The item recommendation system transmits the contrast group responsive to the request.