MULTI-MODAL SYNTHETIC CONTENT GENERATION USING NEURAL NETWORKS

Smart overview of the Invention

The patent application discusses systems and methods for generating multi-modal synthetic content using neural networks. These systems utilize one or more neural networks to produce outputs that reflect creative and artistic qualities based on input prompts. A key feature includes a text extension model that enhances the input prompts with additional information, enabling the generation of high-resolution outputs. These neural networks are designed to support end-to-end conversational interfaces, facilitating the reception of input prompts and the presentation of creative outputs.

Background

Traditional content generation systems, including those powered by machine learning models, produce various types of content like text, speech, audio, and images based on user inputs. However, achieving high-quality content can be challenging due to insufficient training data, especially for artistic or creative content. This application addresses these challenges by proposing innovative systems and methods for generating artistic content across multiple modalities.

Key Features

The proposed systems use generative models, such as diffusion models, to create outputs in multiple modalities like text, audio, speech, images, and video. The systems can process queries in diverse ways to generate high-resolution artistic content effectively. They can also connect with external data sources for model retraining and are capable of supporting conversational AI interfaces for seamless interaction.

Technical Implementation

A processor within the system can handle prompts indicating features and characteristics to determine outputs using neural networks. It maintains these outputs in storage and presents them via display or audio devices. The system can expand text prompts using a text completion model that generates more detailed text data. Outputs can include combinations of synthetically generated data and pre-existing data across different modalities.

Applications and Use Cases

The described processors, systems, and methods can be implemented in various applications such as autonomous vehicle infotainment systems, digital twin operations, collaborative 3D asset creation, deep learning tasks, edge devices, robots, VR/AR/MR content generation, conversational AI operations, and cloud computing environments. This versatility allows for broad application in generating synthetic data and enhancing user interaction through advanced AI capabilities.