Real-Time Face Reenactment Using Keypoint-Based Mobile Rendering
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Solution Overview
Problem
Existing deep learning methods for face reenactment are time-consuming and not suitable for real-time performance on standard mobile devices, limiting their application in scenarios like entertainment and social media.
Innovation Solution
A method and system for text and audio-based real-time face reenactment using neural networks to generate sequences of acoustic features, scenario data, and frames, synthesizing audio and video on mobile devices without requiring server-side computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If deep learning methods are used for face reenactment, then photorealistic results are achieved, but processing time increases and real-time performance is not attainable on mobile devices
Solution Approach 1:
The deep learning model is segmented into multiple smaller neural network modules (acoustic feature network, scenario data network, frame generation network) that can process different aspects of face reenactment independently and in parallel, reducing overall processing time while maintaining photorealistic quality
Solution Approach 2:
Acoustic features are extracted from audio input in advance, and scenario data (mouth keypoints, facial expressions) are pre-computed before actual frame generation, allowing the rendering stage to proceed faster with pre-processed intermediate representations
2Manufacturing precision
If deep learning methods are used for face reenactment, then photorealistic results are achieved, but computational resources required exceed mobile device capabilities
Solution Approach 1:
The computational workload is divided across specialized neural network modules that can be optimized for mobile hardware, with each module handling a specific aspect (acoustic processing, facial landmark detection, image synthesis) to reduce peak resource requirements
Solution Approach 2:
Instead of processing full-resolution images through complex deep learning models, the system uses lightweight networks to generate keypoint representations and scenario data, then applies these to template face images, reducing computational complexity while preserving photorealistic appearance
3Productivity
If real-time processing is implemented on mobile devices, then processing speed increases, but output quality decreases
Solution Approach 1:
Acoustic features and scenario data are pre-computed from audio input before frame generation, allowing the actual video synthesis to use simple template matching and rendering operations that maintain quality while achieving real-time performance
Solution Approach 2:
The system transforms the problem from direct pixel-manipulation deep learning to a parameter-based approach where acoustic features and facial keypoints are computed, then applied to pre-rendered face templates, achieving both speed and quality
Data Source
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AI summary
A computer-implemented method is disclosed. The method receives (1005) an input text and a target image. The target image includes a target face. The method generates (1010), based on the input text, a sequence of sets of acoustic features representing the input text. The method generates (1015), based on the sequence of sets of acoustic features, a sequence of sets of mouth key points. The method generates, based on the sequence of sets of mouth key points, a sequence of sets of facial key points. The method generates (1020), based on the sequence of sets of the facial key points and the target image, a sequence of frames. The frames include the target face modified based on at least one set of mouth key points of the sequence of sets of mouth key points. The method generates (1025), based on the sequence of frames, an output video.