Recurrent Image Unit With Dynamic Warping for Motion Consistency
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Solution Overview
Problem
Existing neural networks struggle to generate or process sequences of images with high motion consistency and accuracy, particularly when objects undergo significant displacement, leading to inconsistencies and suboptimal computational efficiency.
Innovation Solution
A recurrent unit within a neural network that applies convolutional kernels dynamically to warp outputs based on input and previous states, allowing for improved temporal consistency and accuracy in generating or processing image sequences, utilizing adaptive systems like generator networks and discriminator networks for training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing neural networks are used to generate or process image sequences, then basic functionality is provided, but motion consistency and accuracy deteriorate when objects undergo significant displacement
Solution Approach 1:
The patent implements dynamic spatial transformation by applying motion-specific convolution kernels that are selected and applied based on the detected motion characteristics of objects between frames. This allows the system to adaptively transform spatial coordinates according to actual motion patterns, resolving the contradiction between maintaining reliability during basic processing and achieving precision during significant displacement.
Solution Approach 2:
The patent applies different convolution kernels to different regions of the image based on local motion characteristics. By identifying objects with significant displacement and applying motion-specific transformations only to those regions, the system maintains high motion accuracy where needed while preserving overall motion consistency, thus resolving the precision-reliability contradiction.
2Manufacturing precision
If complex processing methods are used to improve motion accuracy, then processing quality improves, but computational efficiency deteriorates
Solution Approach 1:
The patent implements partial processing by applying complex motion-specific convolution operations only to regions containing objects with significant displacement, rather than processing the entire image. This selective approach maintains high motion accuracy for moving objects while significantly reducing overall computational requirements, thus resolving the contradiction between precision and efficiency.
Solution Approach 2:
The patent segments the image processing task by identifying and separating objects with significant displacement from the rest of the scene. By applying sophisticated motion accuracy improvements only to these segmented regions and using simpler processing for static regions, the system achieves high motion accuracy while maintaining computational efficiency.
3Stability of the object's composition
If existing neural networks process image sequences, then basic processing capability is provided, but consistency and realism deteriorate
Solution Approach 1:
The patent implements feedback mechanisms by comparing detected motion characteristics with expected motion patterns and using this information to select appropriate convolution kernels. This feedback loop ensures that spatial transformations maintain temporal consistency across frames while preserving the realism of object motion, thus resolving the contradiction between stability and reliability.
4Ease of manufacture
If standard neural network architectures are used, then implementation simplicity is maintained, but motion feature extraction accuracy deteriorates
Solution Approach 1:
The patent implements a universal convolution kernel selection mechanism that can handle both simple and complex motion patterns using the same basic architecture. By maintaining a set of pre-defined motion-specific kernels and selecting the appropriate one based on detected motion characteristics, the system achieves high motion feature extraction accuracy while keeping the implementation relatively simple and modular.
Data Source
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AI summary
A recurrent unit is proposed which, at each of a series of time steps receives a corresponding input vector and generates an output at the time step having at least one component for each of a two-dimensional array of pixels. The recurrent unit is configured, at each of the series of time steps except the first, to receive the output of the recurrent unit at the preceding time step, and to apply to the output of the recurrent unit at the preceding time step at least one convolution which depends on the input vector at the time step. The convolution further depends upon the output of the recurrent unit at the preceding time step. This convolution generates a warped dataset which has at least one component for each pixel of the array. The output of the recurrent unit at each time step is based on the warped dataset and the input vector.