Microsoft Technology Licensing, LLC

Mingxi Cheng

Ji Li

Amit Srivastava

Dachuan Zhang

Sumithra Bhakthavatsalam

Fatima Zohra Daha

Andi Chen

Veena Gopalakrishna Joshi

Greeshma Marri

Maya Bisineer

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.

Personalized branding with prompt adaptation in large language models and visual language models

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. The trained AI processing converts a formatted template into a feature vector and utilizes conditioned generative modeling to generate one or more transformed templates using a representation of the feature data and feature data from one or more other formatted templates.

Automatic generation of transformations of formatted templates using deep learning modeling

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.

Techniques for generating data for an intelligent gesture detector

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Mingxi Cheng