Animate Static Photos into Talking Videos with LivePortrait AI Compose Perfect Expressions Fast

If you are looking for a way to 1-Click install LivePortrait open source 0-shot image to animation application on Windows, run it locally, this is the tutorial that you need. In this tutorial I am introducing you the state-of-the-art image-to-animation open source generator Live Portrait. Provide your static image, your driving video, and in mere seconds have an amazingly working animation. LivePortrait is extremely fast and also capable of keeping the input video facial expressions. It will blow your mind when you see it. Believe me.

🔗 LivePortrait Installers Scripts ⤵️ ▶️ https://www.patreon.com/posts/105251204

🔗 Requirements Step by Step Tutorial ⤵️ ▶️ https://youtu.be/-NjNy7afOQ0

🔗 Official LivePortrait GitHub Repository ⤵️ ▶️ https://github.com/KwaiVGI/LivePortrait

🔗 SECourses Discord Channel to Get Full Support ⤵️ ▶️ https://discord.com/servers/software-engineering-courses-secourses-772774097734074388

🔗 Paper of LivePortrait: Efficient Portrait Animation with Stitching and Retargeting Control ⤵️ ▶️ https://arxiv.org/pdf/2407.03168

0:00 Introduction to the state-of-the-art image to animation open source application LivePortrait 2:20 How to download and install LivePortrait Gradio application on your computer 3:27 What are the requirements for LivePortrait application and how to install them 4:07 How to verify you have accurately installed requirements or not 5:02 How to verify installation was completed accurately and how to save installation logs 5:37 How to start the LivePortrait application after installation has been completed 5:57 The amazing extra materials I have shared such as portrait images, driving video and rendered videos 7:28 How to use LivePortrait application 8:06 How much VRAM LivePortrait uses when generating 73 seconds long animation video 8:33 Animating first image 8:50 How to monitor the status of animation process 10:10 First animation video is rendered 10:24 What is the resolution of the rendered animation videos 10:45 What is the original output resolution of the LivePortrait 11:27 Which improvements and new features I have coded top of the official demo app 11:51 Where the generated animated videos are saved by default 12:35 The effect of Relative Motion option 13:41 The effect of Do Crop option 14:17 The effect of Paste Back option 15:01 The effect of Target Eyelid Open Ratio option 17:02 How to join SECourses Discord channel

LivePortrait: an innovative framework for animating static portrait images to create realistic and expressive videos. The method aims to balance computational efficiency, generalization ability, and precise controllability.

Key features of LivePortrait:

It builds upon and extends implicit-keypoint-based methods rather than using diffusion-based approaches.

The model is trained in two stages:

Stage I: Base model training with enhancements like high-quality data curation, mixed image-video training, upgraded network architecture, scalable motion transformation, and landmark-guided optimization.

Stage II: Training of stitching and retargeting modules for improved controllability.

The framework introduces three key modules:

Stitching module: Allows seamless integration of animated portraits back into the original image space.

Eyes retargeting module: Enables precise control over eye movements and expressions.

Lip retargeting module: Provides fine-grained control over lip movements.

LivePortrait achieves impressive generation speed, producing animations in just 12.8ms on an RTX 4090 GPU using PyTorch.

The model outperforms many existing methods, including heavy diffusion-based approaches, in terms of generation quality and motion accuracy.

Key contributions:

Development of a solid implicit-keypoint-based video-driven portrait animation framework that significantly enhances generation quality and generalization ability.

Design of advanced stitching and retargeting modules for better controllability, with negligible computational overhead.

Extensive experiments demonstrating the efficacy of the framework in both self-reenactment and cross-reenactment scenarios.

The paper also discusses potential applications of LivePortrait in video conferencing, social media, entertainment, and audio-driven character animations. The authors acknowledge some limitations, such as difficulties with large pose variations in cross-reenactment scenarios and potential jitter with significant shoulder movements.

Noting the potential misuse of portrait animation technologies for deepfakes and the need for responsible use practices. The authors mention that current visual artifacts in synthesized results could aid in deepfake detection.

The paper concludes by highlighting the model's ability to generalize to animal portraits and its potential for portrait video editing. These additional capabilities further demonstrate the versatility and potential of the LivePortrait framework.

#ImageToAnimation #AIAnimation #PortraitAnimation ... https://www.youtube.com/watch?v=FPtpNrmuwXk