Event-Guided Video Frame Interpolation for Nonlinear Motion
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Solution Overview
Problem
Existing video frame interpolation technologies struggle with inaccurate motion prediction in complex scenarios due to assumptions of uniform motion and linear optical flow, and lack of supervision data for training models, leading to artifacts and performance degradation.
Innovation Solution
Utilize dynamic event data from a dynamic vision sensor to estimate nonlinear motion information and perform optical flow estimation, enabling accurate prediction of intermediate frames through a video frame interpolation model trained with cyclic consistency and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If uniform motion assumption and linear optical flow are used for frame interpolation, then the method is simple and fast, but the motion prediction accuracy deteriorates in complex scenarios
Solution Approach 1:
The patent transitions from static uniform motion assumptions to dynamic nonlinear motion modeling. The optical flow estimation network dynamically adapts to complex motion patterns by learning from training data, allowing the system to handle variable motion characteristics in different scenarios while maintaining computational efficiency through learned patterns.
Solution Approach 2:
The patent replaces traditional mechanical/optical flow computation methods with a machine learning-based optical flow estimation network. This substitution enables the system to capture complex nonlinear motion relationships that cannot be modeled by conventional linear optical flow methods, significantly improving motion prediction accuracy in complex scenarios.
2Measurement precision
If supervised training with ground truth intermediate frames is used, then the model training accuracy is improved, but the requirement for training data increases and becomes impractical
Solution Approach 1:
The patent implements self-supervised learning where the model generates its own training supervision signals. By using bidirectional frame interpolation and cyclic consistency constraints, the system creates synthetic ground truth from available low-frame-rate video data, eliminating the need for external high-frame-rate ground truth data while still enabling effective supervised training.
Solution Approach 2:
The patent combines multiple training objectives and loss functions (interpolation loss, cyclic consistency loss, optical flow loss) to create a composite training framework. This multi-component approach allows the model to learn from various aspects of video data simultaneously, achieving high training accuracy without requiring large quantities of labeled ground truth data.
3Speed
If high frame rate video is captured with ordinary camera, then the frame rate is limited by physical mechanism, but the content richness and quality requirements cannot be met
Solution Approach 1:
The patent performs preliminary action by capturing video at the maximum feasible frame rate with ordinary cameras, then applies frame interpolation to generate additional intermediate frames. This two-stage approach allows the system to work within physical camera limitations while still achieving high effective frame rates through computational generation of missing frames.
Solution Approach 2:
The patent introduces an intermediate computational stage that acts as a mediator between the camera's physical frame rate limit and the desired high frame rate output. The frame interpolation model generates intermediate frames that bridge the temporal gaps, effectively multiplying the camera's native frame rate capability without requiring hardware changes.
4Measurement precision
If dynamic event data from dynamic vision sensor is used, then the motion information accuracy is improved, but the device complexity increases
Solution Approach 1:
The patent designs the frame interpolation model to handle multiple data types universally - it can process both conventional video frames and dynamic event data from vision sensors. The model architecture accommodates different input formats and sensor types, allowing the system to leverage specialized sensors when available while maintaining compatibility with standard video inputs, thus justifying the added device complexity through enhanced functionality.
Data Source
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AI summary
Embodiments of this disclosure relate to the multimedia processing field, and provide a video frame interpolation method and apparatus, and a device. In the video frame interpolation method in this disclosure, a first image at first time, a second image at second time, and sensor data captured by a dynamic vision sensor apparatus are obtained, and the sensor data includes dynamic event data between the first time and the second time. At least one target image is determined based on the first image, the second image, and the sensor data, where the at least one target image is an image corresponding to at least one target time between the first time and the second time. The dynamic event data is used to help compensate for motion information missing from existing image data. This implements accurate prediction of an intermediate image, and improves image prediction effect.