Snap inc. (20240296614). PHOTOREALISTIC REAL-TIME PORTRAIT ANIMATION simplified abstract
Contents
PHOTOREALISTIC REAL-TIME PORTRAIT ANIMATION
Organization Name
Inventor(s)
Eugene Krokhalev of London (GB)
Aleksandr Mashrabov of Los Angeles CA (US)
Pavel Savchenkov of London (GB)
PHOTOREALISTIC REAL-TIME PORTRAIT ANIMATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240296614 titled 'PHOTOREALISTIC REAL-TIME PORTRAIT ANIMATION
The patent application describes systems and methods for portrait animation using scenario data and target images.
- Receiving scenario data about movements of a first head and a target image with a second head and background.
- Determining two-dimensional deformations of the second head based on the target image and movement information.
- Applying the deformations to the target image to create output frames with the second head displaced according to the first head's movements.
- Filling background gaps using a background prediction neural network.
Potential Applications: - Entertainment industry for creating animated characters with realistic movements. - Virtual reality and augmented reality applications for interactive experiences. - Educational tools for visualizing complex concepts through animated characters.
Problems Solved: - Enhances the realism of portrait animation by accurately reflecting head movements. - Streamlines the animation process by automating deformations based on movement data. - Improves the overall visual quality of animated content.
Benefits: - Creates more engaging and lifelike animated characters. - Saves time and resources in the animation production process. - Enhances user experience in interactive applications.
Commercial Applications: "Portrait Animation Technology: Revolutionizing Character Animation in Entertainment and Virtual Reality"
Questions about Portrait Animation: 1. How does the technology differentiate between different types of head movements for accurate deformations? 2. What are the potential limitations of using a background prediction neural network for filling in background gaps in the animation process?
Frequently Updated Research: Stay updated on advancements in facial recognition technology and neural network algorithms for improved portrait animation techniques.
Original Abstract Submitted
provided are systems and methods for portrait animation. an example method includes receiving, by a computing device, scenario data including information concerning movements of a first head, receiving, by the computing device, a target image including a second head and a background, determining, by the computing device and based on the target image and the information concerning the movements of the first head, two-dimensional (2d) deformations of the second head in the target image, applying, by the computing device, the 2d deformations to the target image to obtain at least one output frame of an output video, the at least one output frame including the second head displaced according to the movements of the first head, and filling, by the computing device and using a background prediction neural network, a portion of the background in gaps between the displaced second head and the background.
