Text-and-Audio Face Reenactment for Real-Time Mobile Use
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing deep learning methods for face reenactment are time-consuming and not suitable for real-time performance on standard mobile devices.
Innovation Solution
A method and system for text and audio-based real-time face reenactment using a computing device to generate a sequence of frames and audio data, utilizing neural networks to process input text and target images, and applying 2D deformations and mouth texture images to animate the target face in real-time.
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 becomes unachievable
Solution Approach 1:
The patent segments the face reenactment process into multiple independent modules: source face extraction, target face extraction, acoustic feature generation, and face animation. Each module processes specific tasks separately, allowing optimization for both quality and speed. The segmentation enables parallel processing and reduces the computational burden on any single operation, making real-time performance achievable while maintaining photorealistic results.
Solution Approach 2:
The patent implements preliminary action by pre-extracting and storing source face images, target face images, and acoustic features before the actual reenactment process. The system pre-processes the target face by extracting facial landmarks and creating deformation models in advance. This preliminary preparation significantly reduces processing time during real-time operation, as the system only needs to apply pre-computed deformations rather than performing complex calculations in real-time.
2Manufacturing precision
If existing deep learning methods are used, then face reenactment quality is improved, but device complexity increases and mobile device compatibility is lost
Solution Approach 1:
The patent extracts the most computationally intensive operations from the complete deep learning pipeline and implements them as optimized standalone functions that can run on mobile devices. The system extracts key facial features, generates acoustic features, and computes deformations using lightweight algorithms that maintain quality while reducing computational complexity. This extraction allows the system to operate on standard mobile devices without requiring powerful servers.
Solution Approach 2:
The patent applies parameter changes by adjusting the complexity of computational operations based on available hardware resources. The system dynamically modifies parameters such as resolution, number of processing iterations, and algorithm complexity to match the capabilities of mobile devices. This enables the system to achieve photorealistic results on constrained hardware by optimizing parameters for balance between quality and performance.
3Speed
If server-side computational resources are used, then processing speed is improved, but system independence and privacy are reduced
Solution Approach 1:
The patent implements self-service by designing the face reenactment system to execute all computational operations locally on the user's device without requiring server-side processing. The system independently extracts faces, generates acoustic features, computes deformations, and produces final output using only the device's own computational resources. This self-service approach eliminates dependency on external servers, enhancing privacy and system independence while maintaining adequate processing speed through optimized local algorithms.
Data Source
AI summary
Systems and methods for text and audio-based real-time face reenactment are provided. An example method includes receiving an input text and a target image, where the target image includes a target face, generating, based on the input text, a sequence of acoustic feature sets, generating, based on the sequence of acoustic feature sets, a sequence of mouth texture images, inserting a mouth texture image of the sequence of mouth texture images into a mouth region of the target face to produce an output frame of a sequence of output frames, and generating an output video including the sequence of output frames.