Video Extension Using Motion-Guided Temporal Frame Generation
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Solution Overview
Problem
Conventional video editing methods result in visually inconsistent and jarring transitions due to the lack of motion information across frames, leading to inefficient use of computing resources in adjusting low-quality video extension frames.
Innovation Solution
A text-to-video generative model that captures motion information across multiple frames to generate temporally coherent video extension frames, using multi-task learning and supervised training to ensure consistency with the original video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If conventional video editing methods are used to extend video duration, then video transitions can be added, but the generated frames lack motion information across frames resulting in visually inconsistent and jarring transitions
Solution Approach 1:
The patent transitions from single-frame generation to multi-frame temporal modeling by introducing temporal dimensions. The system processes sequences of frames and captures motion information across multiple time steps, transforming the problem from spatial-only (single frame) to spatio-temporal (multiple frames), thereby achieving visual consistency in video extensions.
Solution Approach 2:
The system performs preliminary extraction of motion information from input video frames before generating extension frames. By analyzing motion patterns, optical flow, and temporal relationships in advance, the model prepares motion constraints that guide the generation process, ensuring that extended frames maintain visual consistency with the original video's motion characteristics.
2Productivity
If video extension frames are generated without motion information, then generation speed may be faster, but computing resources are wasted adjusting low-quality frames
Solution Approach 1:
The system implements feedback mechanisms where motion information extracted from input frames is fed back into the generation process. The model uses extracted motion patterns, optical flow fields, and temporal constraints as feedback signals to guide frame generation, ensuring that generated frames align with the original video's motion dynamics and reducing the need for post-generation adjustments.
Solution Approach 2:
By performing preliminary extraction and analysis of motion information before the main generation process, the system prepares motion constraints and temporal guidelines in advance. This preliminary action ensures that the generation process starts with accurate motion guidance, reducing iterations and adjustments needed later, thereby optimizing both speed and resource efficiency.
3Device complexity
If single-frame generation models are used, then the system is simpler, but the generated content lacks motion information and appears visually inconsistent
Solution Approach 1:
The patent segments the video generation task into distinct functional components: motion information extraction from input frames, temporal pattern analysis, and guided frame generation. By dividing the complex spatio-temporal modeling into separate modules that handle motion analysis and generation independently, the system achieves temporal coherence while managing model complexity through modular architecture.
Solution Approach 2:
The system introduces motion information and temporal constraints as intermediary elements between the input video and generated frames. These intermediaries (motion fields, optical flow, temporal guidelines) act as mediators that carry motion semantics from the input video to guide the generation process, enabling temporal coherence without requiring the entire model to be excessively complex.
Data Source
AI summary
Embodiments are disclosed for generating a temporally coherent video extension. The method includes displaying, on a graphical user interface, a user interface element representing a video to be extended, where the video includes a number of frames. The method further includes receiving an input via the graphical user interface associated with the user interface element. The input causes a visual change to the user interface element which represents a duration of an extension to be made to the video. The method further includes generating frames based on the duration of the extension. The generated frames use motion information determined from frames of the video. The motion information represents a per-pixel motion between at least a pair of frames of the video. The method further includes providing, for display on the graphical user interface, an extended video including the frames of the video and the generated frames.