PaddlePaddle GAN library, including lots of interesting applications like First-Order motion transfer, Wav2Lip, picture repair, image editing, photo2cartoon, image style transfer, GPEN, and so on.

Real time interactive streaming digital human

Wav2Lip UHQ extension for Automatic1111

High quality Lip sync

PyTorch Implementation for Paper "Emotionally Enhanced Talking Face Generation" (ICCVW'23 and ACM-MMW'23)

lipsync is a simple and updated Python library for lip synchronization, based on Wav2Lip. It synchronizes lips in videos and images based on provided audio, supports CPU/CUDA, and uses caching for faster processing.

Alternative to Flawless AI's TrueSync. Make lips in video match provided audio using the power of Wav2Lip and GFPGAN.

(Windows/Linux/MacOS) Local WebUI with neural network models (Text, Image, Video, 3D, Audio) on python (Gradio interface). Translated on 3 languages

Wav2Lip UHQ Improvement with ControlNet 1.1

Orchestrating AI for stunning lip-synced videos. Effortless workflow, exceptional results, all in one place.

AI Lip Syncing application, deployed on Streamlit

GUI to sync video mouth movements to match audio, utilizing wav2lip-hq. Completed as part of a technical interview.

This project is dedicated to advancing the field of animatronic robots by enabling them to generate lifelike facial expressions, pushing the boundaries of what's possible in human-robot interaction.

IN4U - 면접 연습 웹 서비스

AIStreameur : Faite streamer vos personne préféré !

基于飞桨开发的虚拟主播。扩展多语言检测，语音，人脸增强

The LipSync-Wav2Lip-Project repository is a comprehensive solution for achieving lip synchronization in videos using the Wav2Lip deep learning model. This open-source project includes code that enables users to seamlessly synchronize lip movements with audio tracks.

Version of original Wav2Lip quick trial notebook working directly in Drive folder and fixing a naming error

This repository hosts the code used by Apollo during Wav2Lip's inference process.

Create realistic talking head videos by converting input text to speech and lip-syncing it with a still image using Wav2Lip. This project automates the entire pipeline: it generates audio from text and then syncs it with facial movements in the image, producing a natural-looking video output.