Consistent resampling factors and adaptive resampling factors for features in generative face video compression
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Solution Overview
Problem
Existing facial video compression techniques, such as Generative Face Video Compression (GFVC), are limited to coding video sequences with a resolution of 256×256, failing to accommodate the growing demand for higher resolution content driven by high-definition displays and immersive visual experiences.
Innovation Solution
Implementing consistent and adaptive resampling factors in GFVC to enable processing of heterogeneous-resolution video sequences, using interpolation of heterogeneous-resolution sequences, multi-scale architecture of feature extractors, and dynamic neural networks to adapt to varying input resolutions, allowing transcoding across a wider range of resolutions including 512×512, 1024×1024, and 1920×1024.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If GFVC is limited to fixed resolution (256×256) coding, then implementation simplicity is maintained, but adaptability to heterogeneous resolutions deteriorates
Solution Approach 1:
The patent implements dynamic resampling factors that adapt to different input resolutions. The system calculates resampling factors based on the ratio between input resolution and reference resolution, enabling the GFVC model to dynamically adjust to heterogeneous resolutions (256×256, 512×512, 1024×1024, 1920×1024) without requiring separate models for each resolution, thus achieving resolution adaptability while maintaining reasonable system complexity
Solution Approach 2:
The patent changes the parameter of resampling factor from fixed to variable. By computing resampling factors as ratios (e.g., 0.25 for 256×256, 0.5 for 512×512, 1.0 for 1920×1024), the system transforms the rigid fixed-resolution constraint into a flexible parameter-based approach, allowing the same GFVC model to process multiple resolutions effectively
2Manufacturing precision
If resolution is increased for high-definition displays, then visual quality is improved, but compression efficiency deteriorates
Solution Approach 1:
The patent segments the video processing into distinct stages: feature extraction at original resolution, resampling to reference resolution, GFVC compression, and reconstruction. This segmentation allows the system to maintain high reconstruction quality at various resolutions while managing data volume efficiently through selective processing at different resolution levels
Solution Approach 2:
The patent introduces a resolution dimension by implementing multi-resolution support with resampling factors. Instead of processing all videos at a single high resolution, the system operates in a multi-dimensional resolution space, transforming videos to a reference resolution for compression and then reconstructing at various resolutions, thereby achieving quality improvement without proportional data volume increase
Data Source
AI summary
Generative Face Video Compression (“GFVC”) techniques are provided to improve performance of facial video compression. A computing system is configured to perform GFVC upon heterogeneous-resolution sequences based on consistent resampling factors and based on adaptive resampling factors. Adaptive resampling factors are further implemented by: interpolation of heterogeneous-resolution sequences in GFVC to simplify resolution unification; multi-scale architecture of feature extractors in GFVC to capture details across heterogeneous resolutions by integrating multiple processing layers; and adapting dynamic neural networks in real-time to process varying input resolutions of heterogeneous-resolution sequences in GFVC efficiently.