SYSTEMS AND METHODS FOR IMAGE GENERATION WITH MACHINE LEARNING MODELS

A method is described for improving parts of an image using machine learning. First, a digital image is taken, and a specific area of it is removed to create a masked image. Then, a text description is provided that explains how the image should be enhanced. This information, along with the original image and the masked area, is fed into a machine learning model. Finally, the model uses this data to create a new, improved version of the image.

SYSTEMS AND METHODS TO INTEGRATE VISUAL AND AUDIO REPRESENTATIONS OF REAL-WORLD USERS INTO EXISTING MEDIA CONTENT

A new online system lets users change how they look and sound in videos and games. By using advanced technology, people can replace the faces and voices of actors with their own or those of celebrities. This can be done in movies, TV shows, animations, music videos, and video games. The system works smoothly with different streaming services and platforms. It aims to make entertainment more personal and customizable for everyone.

A CHATBOT FOR DEFINING A MACHINE LEARNING (ML) SOLUTION

An intelligent assistant, like a chatbot, helps users create machine learning systems. It is designed for people who may not know much about software development or machine learning. Users can communicate with the chatbot through voice, text, or visual interfaces. The chatbot understands everyday language and converts it into a structured format for machine learning solutions. This allows anyone to work with artificial intelligence and develop machine learning models without needing to be a data scientist.

GENERATING 3D MODELS FROM A SINGLE IMAGE

A new system can create a 3D model using just one picture. It starts by taking an image and some details about how the photo was taken. Then, it breaks down the image into smaller parts to understand its 2D features. After that, it uses the camera information to convert these 2D features into 3D features. Finally, a complete 3D model is built based on these features.

LOCAL IMAGE AND SCENE EDITING BY TEXT INSTRUCTIONS

A new method allows users to edit specific parts of an image using text instructions. First, the user provides an image and a description of what they want to change. The system then creates a map that shows how relevant the text is to different areas of the image. Using this map, it edits the image accordingly to reflect the user's request. Finally, the edited image is shown back to the user.

Adapting a Virtual Reality Experience for a User Based on a Mood Improvement Score

A virtual reality application creates experiences designed to boost a user's mood. It uses a collection of digital assets, each with a score that predicts how much they can improve mood for similar users. By analyzing a user's profile and these scores, the app customizes the virtual reality experience just for them. The user's mood changes are tracked through surveys or biometric data. Over time, the app updates the scores based on how effective each digital asset is for different users.

CONTROLLABLE DIFFUSION MODEL

A new method helps create images by combining different layouts and styles. Users can provide a specific layout and a desired style through an interface or select them from a database. The system processes the layout to create a mask that represents it. It also processes the style to create an embedding that captures its features. Finally, the image generation model combines both the layout mask and style embedding to produce a new image that reflects the chosen layout and style.

UTILIZING A GENERATIVE NEURAL NETWORK TO INTERACTIVELY CREATE AND MODIFY DIGITAL IMAGES BASED ON NATURAL LANGUAGE FEEDBACK

A new system uses a special type of computer program called a neural network to create and change digital images based on written descriptions. Users can interact with this system by providing text feedback, allowing for multiple rounds of image generation and editing. The program understands the user's words and uses them to influence how the images look. It combines the information from the text with the images to ensure that the final result matches what the user wants. Additionally, the system keeps important details consistent across different images during the editing process.

BODY TRACKING FROM MONOCULAR VIDEO

Body tracking can be done using regular video footage of a person. First, the video is analyzed to extract images that show the person moving. These images are then fed into a trained computer program that figures out the person's body position. Next, the program creates a 3D model of the person's upper body joints and rates how confident it is about each joint's position. Finally, this information is used to animate a 3D character that mimics the person's movements, which can be shown on a screen.

AUGMENTED REALITY DISPLAY WITH FRAME MODULATION FUNCTIONALITY

A head-mounted display system can track how it moves in relation to things around the user. It checks if the display itself is moving or if objects in the environment are changing position. This helps the system understand where to place virtual images so they look realistic to the user. By knowing where both the display and objects are, it can adjust what the user sees. Overall, this technology enhances the experience of augmented reality by making virtual images appear more naturally in the real world.