Samsung electronics co., ltd. (20240354996). AUTOREGRESSIVE CONTENT RENDERING FOR TEMPORALLY COHERENT VIDEO GENERATION simplified abstract
Contents
AUTOREGRESSIVE CONTENT RENDERING FOR TEMPORALLY COHERENT VIDEO GENERATION
Organization Name
Inventor(s)
Varun Menon of Mountain View CA (US)
Siddarth Ravichandran of Santa Clara CA (US)
Ankur Gupta of San Jose CA (US)
Hyun Jae Kang of Mountain View CA (US)
Sajid Sadi of San Jose CA (US)
AUTOREGRESSIVE CONTENT RENDERING FOR TEMPORALLY COHERENT VIDEO GENERATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240354996 titled 'AUTOREGRESSIVE CONTENT RENDERING FOR TEMPORALLY COHERENT VIDEO GENERATION
Abstract: Autoregressive content rendering for temporally coherent video generation involves generating a plurality of predicted images using an autoencoder, which are then fed back into the network. The predicted images can be encoded to create encoded predicted images, along with encoding a plurality of keypoint images to generate encoded keypoint images. The autoencoder network then decodes selected encoded keypoint images with encoded predicted images from a prior iteration to generate one or more predicted images.
- Simplified Explanation:
The technology described in the patent application involves using an autoencoder to generate a series of predicted images for video generation, ensuring temporal coherence.
- Key Features and Innovation:
- Utilizes an autoencoder network to generate and refine predicted images for video generation - Encodes and decodes keypoint images to improve the accuracy of the predicted images - Enables the creation of temporally coherent videos through iterative refinement of predicted images
- Potential Applications:
- Video editing and post-production - Animation and special effects in movies and TV shows - Virtual reality and augmented reality content creation
- Problems Solved:
- Ensures temporal coherence in generated videos - Improves the quality and accuracy of predicted images for video generation - Streamlines the process of creating visually appealing videos
- Benefits:
- Enhances the visual quality of generated videos - Simplifies the video generation process - Enables the creation of realistic and engaging video content
- Commercial Applications:
Autoregressive content rendering technology can be applied in industries such as film production, advertising, virtual reality development, and video game design to streamline the creation of high-quality, visually appealing content.
- Questions about Autoregressive Content Rendering for Temporally Coherent Video Generation:
1. How does the autoencoder network improve the accuracy of predicted images? 2. What are the potential applications of this technology beyond video generation?
- Frequently Updated Research:
Researchers are continually exploring ways to enhance the efficiency and accuracy of autoregressive content rendering for video generation, with a focus on real-time applications and scalability.
Original Abstract Submitted
autoregressive content rendering for temporally coherent video generation includes generating, by an autoencoder, a plurality of predicted images. the plurality of predicted images is fed back to the autoencoder network. the plurality of predicted images may be encoded by the autoencoder network to generate a plurality of encoded predicted images. the autoencoder network encodes a plurality of keypoint images to generate a plurality of encoded keypoint images. one or more predicted images of the plurality of predicted images are generated by the autoencoder network by decoding a selected encoded keypoint image of the plurality of encoded keypoint images with an encoded predicted image of the plurality of encoded predicted images of a prior iteration of the autoencoder network.
