Abstract: A staged approach for mitigating color inconsistencies arising from Diff-Edit based inpainting may include blended masked-maskless inpainting, dynamic mask modification, and/or mask boundary latent value smoothing. Blended masked-maskless inpainting mitigates color inconsistencies by blending masked inpainting with maskless inpainting during iterative denoising steps. Dynamic mask modification serves to iteratively modify the mask in the spatial dimension during iterative denoising steps. Mask boundary latent value smoothing may be applied to smooth latent values at the edge of the masked region.

Abstract: A data processing system implements techniques for generating personalized content using a brand kit. The system receives a natural language prompt to generate content in a design application on the client device of a user and analyzes the prompt to determine whether the user intends to apply a brand kit to the generated content. The system automatically generates a brand kit for the user if one does not already exist and applies the brand kit to content generated using one or more generative models to create personalized content. The system includes a prompt generation unit that generates a plurality of model-specific prompts to the one or more generative models to cause the one or more generative models to create the personalized content.

Abstract: A data processing system implements receiving a style request including a style image and image(s) of subject(s) for generating an avatar for the subject(s); constructing a first prompt by appending the style request and the image(s) to a first instruction string, the first instruction string including instructions to a multimodal model to generate a textual description of the subject(s) from the image(s), to generate a textual description of a style from the style image, and to construct a second prompt including instructions to a text-to-image model to create the avatar for the subject(s) in the style based on the textual descriptions; providing the first prompt to the multimodal model and receiving the second prompt; providing the second prompt to the text-to-image model and receiving the avatar; providing the avatar to the client device; and causing the user interface of the client device to display the avatar.

Abstract: A data processing system implements receiving a first prompt including a style visual content item and a topic content item and requesting generating an output visual content item; constructing a second prompt as an input to a first generative model, by appending the style visual content item and the topic content item to a first instruction string that comprises instructions to the first generative model to generate a textual description combining a topic in the topic content item with a style in the style visual content item as a third prompt; inputting the third prompt into a second generative model to generate the output visual content item by including the topic in the output visual content item and replacing visual element(s) of the style visual content item based on the topic while preserving the style; and providing the output visual content item to be presented on a user interface.

Abstract: A data processing system includes a processor, and a memory storing executable instructions which, when executed by the processor, cause the processor alone or in combination with other processors to perform the following functions: based on a list of design purposes, generate prompts requesting a Large Language Model (LLM) to produce corresponding prompts for input to a text-to-image model to generate a proposed design corresponding to each design purpose; submit the prompts from the LLM to the text-to-image model; receive the proposed designs from the text-to-image model; and increase a design template library by adding a design based on the proposed designs output by the text-to-image model.

Abstract: A data processing system implements techniques for generating personalized content using a brand kit. The system receives a natural language prompt to generate content in a design application on the client device of a user and analyzes the prompt to determine whether the user intends to apply a brand kit to the generated content. The system automatically generates a brand kit for the user if one does not already exist and applies the brand kit to content generated using one or more generative models to create personalized content. The system includes a prompt generation unit that generates a plurality of model-specific prompts to the one or more generative models to cause the one or more generative models to create the personalized content.

Abstract: Applicant discloses herein relate to systems, methods, apparatuses, and non-transitory computer readable media for generating physiological signals datasets, analyzing physiological signals in the physiological signals datasets, extract fractional dynamics signatures specific to Chronic Obstructive Pulmonary Disease (COPD) medical records, and identifying, using a deep neural network (DNN), a COPD stage.

Abstract: A system for cropping an image is disclosed, which performs receiving a source image and user intention data; determining a target feature based on the user intention data; identifying a plurality of visual features within the source image; determining a contextual relevance between the target feature and each identified visual feature of the source image; identifying, based on the determined contextual relevance between the target feature and each identified visual feature of the source image, one or more cropping candidate portions within the source image; cropping, based on the one or more cropping candidate portions, the source image to generate a plurality of cropped images; and causing the plurality of cropped images to be displayed on a display.

Abstract: The present disclosure applies trained artificial intelligence (AI) processing adapted to automatically generating transformations of formatted templates. Pre-existing formatted templates (e.g., slide-based presentation templates) are leveraged by the trained AI processing to automatically generate a plurality of high-quality template transformations. In transforming a formatted template, the trained AI processing not only generates feature transformation of objects thereof but may also provide style transformations where attributes associated with a presentation theme may be modified for a formatted template or set of formatted templates. The trained AI processing is novel in that it is tailored for analysis of feature data of a specific type of formatted template.

Abstract: A method and system for generating training data for training a gesture detection machine-learning (ML) model includes receiving a request to generate training data for the gesture detection model, the training data being associated with a target gesture, retrieving data associated with an original gesture, the original gesture being a gesture made using a body part, retrieving skeleton data associated with the target gesture, the skeleton data displaying a skeleton representative of the body part and the skeleton displaying the target gesture, aligning a location of the body part in the data with a location of the skeleton in the skeleton data, providing the aligned data and the skeleton data to an ML model for generating a target data that displays the target gesture, receiving the target data as an output from the ML model, the target data preserving a visual feature of the data and displaying the target gesture, and providing the target data to the gesture detection ML model.

Abstract: The present disclosure applies trained artificial intelligence (AI) processing adapted to automatically generating transformations of formatted templates. Pre-existing formatted templates (e.g., slide-based presentation templates) are leveraged by the trained AI processing to automatically generate a plurality of high-quality template transformations. In transforming a formatted template, the trained AI processing not only generates feature transformation of objects thereof but may also provide style transformations where attributes associated with a presentation theme may be modified for a formatted template or set of formatted templates. The trained AI processing is novel in that it is tailored for analysis of feature data of a specific type of formatted template.