Video Segmentation Using Latent Diversity Feature Decomposition
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Solution Overview
Problem
Current rotoscoping pipelines in the film industry are labor-intensive and costly, requiring manual editing and segmentation of video frames by graphics artists, which is inefficient for high-resolution video processing.
Innovation Solution
A neural network-based approach using latent diversity dense feature decomposition with a boundary loss function for automated video segmentation, enabling the network to segment objects from high-resolution videos at native resolution with minimal user input, and improving segmentation quality by focusing on boundary pixels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual editing and segmentation of video frames is performed by graphics artists, then segmentation quality can be controlled, but the process becomes labor-intensive and costly
Solution Approach 1:
The patent replaces manual mechanical editing processes with an automated neural network system. The neural network performs video segmentation automatically, substituting the mechanical manual labor of graphics artists with an automated computational system that processes video frames to segment objects based on learned patterns and features.
Solution Approach 2:
The neural network system performs self-service by automatically learning and applying segmentation patterns without requiring continuous human intervention. The system processes video data independently, making segmentation decisions based on its trained models, thereby eliminating the need for labor-intensive manual editing while maintaining segmentation quality.
2Productivity
If automated segmentation is implemented, then processing efficiency improves, but segmentation accuracy may deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms in the neural network through loss functions that compare automated segmentation results with ground truth labels. This feedback loop enables the system to learn from errors and continuously improve segmentation accuracy, ensuring that automated processing achieves both efficiency and precision by adjusting parameters based on performance metrics.
Solution Approach 2:
The system employs parameter changes through dynamic adjustment of neural network hyperparameters and segmentation thresholds. By optimizing parameters such as learning rates, batch sizes, and segmentation sensitivity levels, the system adapts to different video content characteristics, maintaining high segmentation accuracy while achieving automated processing efficiency.
3Measurement precision
If high-resolution video processing is performed, then segmentation detail improves, but computational complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the video processing task into multiple manageable components. The neural network processes video frames through staged computations, segmenting the complex high-resolution processing into sequential operations that handle different aspects of segmentation independently, thereby reducing overall computational complexity while maintaining high segmentation detail.
Solution Approach 2:
The system manages computational complexity by transitioning to another dimension of processing. The neural network transforms 2D video frames into enriched feature representations through multiple processing layers, adding dimensional complexity in the feature space rather than increasing computational burden in the original image space, thereby maintaining segmentation detail with manageable computational complexity.
Data Source
AI summary
Methods, systems and apparatuses may provide for technology that trains a neural network by inputting video data to the neural network, determining a boundary loss function for the neural network, and selecting weights for the neural network based at least in part on the boundary loss function, wherein the neural network outputs a pixel-level segmentation of one or more objects depicted in the video data. The technology may also operate the neural network by accepting video data and an initial feature set, conducting a tensor decomposition on the initial feature set to obtain a reduced feature set, and outputting a pixel-level segmentation of object(s) depicted in the video data based at least in part on the reduced feature set.