SYNCVISION: High-Resolution Speech-Synchronized Portrait Animation Using Conditional Generative Adversarial Networks

SYNCVISION is a deep learning-based system designed to generate high-resolution speech-synced portrait animations. By utilizing the pretrained Wav2Lip model, it aligns lip movements in portrait images with corresponding audio input, ensuring high-quality animations with computational efficiency. The system is optimized to handle various accents, languages, and visual contexts, making it versatile across different scenarios. SYNCVISION is evaluated using metrics like Mean Absolute Error (MAE) and Lip Synchronization Error (LSE), as well as subjective quality assessments. This project aims to provide a scalable solution for creating realistic speech-driven animations in applications like entertainment, education, and virtual communication.