Live-portrait
Live Portrait transforms a single static photo into lifelike animated portraits with realistic facial expressions, smooth head movements, and precise lip and eye control, using an implicit-keypoint-based framework and stitching technology.
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Quick Overview
Best for: Video
What it does
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Best fit
Video
Pricing snapshot
Free
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Live-portrait
Live Portrait is a video-driven portrait animation framework that converts a single source image into realistic animated videos. It is designed for high generalization, controllability, and efficiency, enabling users to synthesize lifelike facial expressions, head motion, and lip synchronization from driving videos, audio, text, or generated motion. The system is suitable for researchers, developers, and creators who want to animate still photos, perform portrait video editing, or experiment with self- and cross-reenactment scenarios.
Live Portrait transforms a single static photo into lifelike animated portraits with realistic facial expressions, smooth head movements, and precise lip and eye control, using an implicit-keypoint-based framework and stitching technology.
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Claim this listingKey Features
Multi-Style Portrait Animation
Animates still images across various styles (realistic, oil painting, sculpture, 3D rendering) and sizes using stitching technology to produce stylistic variations.
Portrait Video Editing with Stitching
Provides dynamic video-to-video face animation and editing capabilities using stitching modules to refine and retarget outputs.
Precise Eye Animation Control
Dynamically adjusts eye openness using scalar inputs to enable nuanced control over eye expressions and blinking.
Accurate Lip Movement Control
Fine-tunes lip positions with scalar-based inputs to produce precise speech and expression animation and better lip-sync quality.
Advanced Self-reenactment
Generates high-quality animations from a single source frame, outperforming existing methods in recreating dynamic video sequences for the same subject.
Powerful Cross-reenactment
Accurately transfers motion between different portraits, handling large pose changes, subtle expressions, and multi-person inputs.
Implicit Keypoint Animation
Uses an implicit keypoint framework to map motion from a driving video onto the portrait, enabling efficient and controllable animation.
High Efficiency Video Synthesis
Optimized decoder architecture generates frames rapidly while preserving source identity; reported generation speed is 12.8 ms per frame on an RTX 4090 (PyTorch).
Stitching and Retargeting Modules
Small MLP-based stitching and retargeting modules refine outputs for fine-grained control over facial expressions and movements with low computational overhead.
Pricing
A free playground is available at https://live-portrait.org/free-playground where users can try the system for free; no commercial pricing details are provided on the site.
Use Cases
Self-reenactment
Animate a subject using motion derived from another video of the same person to recreate expressions and head movements.
Cross-reenactment
Transfer facial motion from one person (driving video) to a different portrait source while preserving the target's appearance.
Portrait Animation from Still Images
Turn single static photos into lifelike moving portraits for social media, storytelling, or digital exhibits.
Portrait Video Editing
Edit and reanimate portrait videos using stitching and retargeting to change expressions, poses, or style.
Eye and Lip Retargeting Control
Precisely control eye openness and lip positioning for nuanced expression control or improved lip-sync in generated videos.
Style Transfer and Artistic Rendering
Animate portraits across different artistic styles such as oil painting or 3D-rendered looks.
Fine-tuning for Non-human Subjects
Can be fine-tuned for animal animation (e.g., cats, dogs, pandas) as an extended application scenario.
Integrations
GitHub (Inference code & models)
The project provides inference code and trained models at https://github.com/KwaiVGI/LivePortrait for developers to run or modify locally.
PyTorch
Model training and inference use PyTorch (performance benchmark reported with PyTorch on RTX 4090).
Benefits
Limitations
Frequently Asked Questions
What is Live Portrait?
What are the main features of Live Portrait?
Who developed Live Portrait?
What core technologies does Live Portrait use?
What are the application scenarios for Live Portrait?
What is the performance of Live Portrait?
How can I get more information about the Live Portrait open-source project?
How can I start using Live Portrait for free?
Getting Started
- 1 Step 1: Upload a portrait image with a clear face — Live Portrait will extract facial features from this image.
- 2 Step 2: Select or upload a driving video that demonstrates the facial movements you want to transfer.
- 3 Step 3: Click 'Animate', adjust configuration parameters if desired (or use defaults), then generate and wait for the animation to complete.
Support
Contact support at [email protected] for inquiries.
docs
Project inference code, models, and additional documentation are available on GitHub: https://github.com/KwaiVGI/LivePortrait.
playground
Try the system and experiment via the free playground at https://live-portrait.org/free-playground.
API
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