Messaging Video Synthesis Using GAN Motion-Content Separation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current video synthesis methods require substantial computational resources and longer training times, particularly on low-resource devices, and struggle to produce high-resolution, temporally consistent videos efficiently.
Innovation Solution
A multi-part framework using a pre-trained image generator and a motion generator within a generative adversarial network, disentangling motion and content, to synthesize high-resolution, temporally consistent videos with reduced computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If video processing is performed using existing techniques, then video functionality can be added to messaging applications, but the processing speed is insufficient and user experience is degraded
Solution Approach 1:
The system segments video processing into multiple independent components: receiving video data, encoding video data, synthesizing video messages, and transmitting video data. These segmented operations can be processed in parallel through multiple communication channels, significantly improving processing speed while maintaining quality standards for each individual component.
Solution Approach 2:
The patent introduces a new dimension of parallel processing by utilizing multiple communication channels simultaneously for video message synthesis and transmission. This multi-dimensional approach allows concurrent processing of multiple video streams, thereby increasing overall processing speed without compromising the quality reliability of individual video messages.
2Productivity
If video data is processed through multiple communication channels, then processing speed increases, but system complexity increases
Solution Approach 1:
The messaging system is designed with universal components that can handle multiple functions: the same encoding module processes video data regardless of which communication channel is used, and the synthesis mechanism works uniformly across different channels. This multi-functionality allows the system to increase productivity through parallel processing without proportionally increasing complexity, as the same components are reused across multiple channels.
Solution Approach 2:
The system uses copying by replicating communication channels rather than creating complex new processing logic for each channel. Each channel is a copy of the proven working architecture, which simplifies the overall system design while enabling parallel processing. The standardized copied structures reduce the complexity burden that would otherwise arise from customizing each processing path.
3Loss of time
If video encoding is performed in real-time, then user experience is improved, but computational resources are consumed
Solution Approach 1:
The system performs preliminary actions by pre-processing video data through encoding before synthesis and transmission. Video clips are encoded in advance using standardized formats, which reduces the computational burden during real-time message assembly. This preliminary encoding minimizes the time and resources needed for final video message preparation while maintaining real-time responsiveness.
Solution Approach 2:
The patent applies parameter changes by adjusting video encoding parameters such as resolution, frame rate, and compression level based on available computational resources and user requirements. This allows the system to optimize the balance between real-time processing capability and resource consumption, achieving acceptable video quality with reduced computational overhead by adapting encoding parameters to current system conditions.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Aspects of the present disclosure involve a system comprising a computer-readable storage medium storing a program and method for video synthesis. The program and method provide for accessing a primary generative adversarial network (GAN) comprising a pre-trained image generator, a motion generator comprising a plurality of neural networks, and a video discriminator; generating an updated GAN based on the primary GAN, by performing operations comprising identifying input data of the updated GAN, the input data comprising an initial latent code and a motion domain dataset, training the motion generator based on the input data, and adjusting weights of the plurality of neural networks of the primary GAN based on an output of the video discriminator; and generating a synthesized video based on the primary GAN and the input data.