Video conferencing method
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Solution Overview
Problem
Current video conferencing technologies face challenges in efficiently transmitting high-quality video feeds with low bandwidth and latency, often compromising on realism and authenticity of facial expressions due to the need for high-bandwidth data connections.
Innovation Solution
A method that extracts facial landmark constellations and muscle action encodings from video feeds, compresses them into lightweight containers, and uses a synthetic face generator to reconstruct photorealistic, emotion-authentic images in near real-time, reducing bandwidth requirements and latency while maintaining high-quality video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video feed transmission is used, then high-quality video can be transmitted, but bandwidth consumption increases and latency increases
Solution Approach 1:
The patent extracts only the essential facial features (landmarks and muscle actions) from the complete video feed, transmitting these extracted features instead of the entire video stream. This selective extraction maintains video quality while dramatically reducing bandwidth consumption.
Solution Approach 2:
The video feed is segmented into discrete facial landmarks and muscle action units, which are then independently encoded and transmitted. This segmentation allows for efficient compression and reconstruction of facial expressions without transmitting redundant video data.
2Measurement precision
If traditional video feed transmission is used, then high-quality video can be transmitted, but latency increases
Solution Approach 1:
By extracting only critical facial feature data rather than transmitting complete video frames, the system reduces data transmission time and processing overhead, thereby reducing latency while preserving essential video quality information.
Solution Approach 2:
Facial landmarks and muscle actions are pre-identified and encoded in advance during the video capture phase, allowing for faster transmission and reconstruction without real-time computational delays.
3Productivity
If bandwidth is reduced, then transmission efficiency improves, but realism and authenticity of facial expressions deteriorate
Solution Approach 1:
A synthetic face generator acts as an intermediary that receives compressed facial landmark and muscle action data, then reconstructs photorealistic facial expressions. This mediator bridges the gap between low-bandwidth input and high-quality visual output.
Solution Approach 2:
The system creates synthetic copies of facial expressions by mapping extracted muscle actions onto a digital face model, generating photorealistic representations without transmitting the original video data.
Data Source
AI summary
One variation of a method for video conferencing includes, during a setup period: accessing a test video feed; and generating a face model, representing facial characteristics of a first user depicted in the test video feed, based on features detected in the test video feed. The method also includes, during an operating period: accessing a video feed; representing constellations of facial landmarks, detected in frames in the video feed, in a feed of facial landmark containers; representing sets of facial muscle actions, detected in frames in the video feed, in a feed of facial expression containers; and transforming the feed of facial landmark containers, the feed of facial expression containers, and the face model into a feed of synthetic face images according to the synthetic face generator.