Live-portrait

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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#81 in Video (81 tools)
Added 3 months ago
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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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Key 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

Free Tier Available

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

Create realistic animated portraits from a single image, reducing the need for multi-frame capture or complex rigging.
High controllability with scalar inputs for eyes and lips plus stitching modules for fine-grained expression retargeting.
Efficient frame synthesis (reported 12.8 ms/frame on RTX 4090) enabling fast generation and experimentation.

Limitations

No commercial pricing or licensing details are provided on the site (only a free playground is referenced).
Detailed security, privacy, and deployment constraints are not specified on the public pages; users should consult the GitHub repository and project documentation for implementation details.

Frequently Asked Questions

What is Live Portrait?
Live Portrait is a video-driven portrait animation framework that focuses on better generalization, controllability, and efficiency for practical usage. It's designed to synthesize lifelike videos from a single source image.
What are the main features of Live Portrait?
Main features include generating realistic videos from a single source image with motion derived from driving videos, audio, text, or generation. It supports self-reenactment, cross-reenactment, eye and lip retargeting control, and multiple styles (realistic, oil painting, sculpture, 3D rendering).
Who developed Live Portrait?
Live Portrait was developed by researchers from Kuaishou Technology, University of Science and Technology of China, and Fudan University.
What core technologies does Live Portrait use?
It extends an implicit-keypoint-based framework, uses a mixed image-video training strategy, upgraded network architecture, improved motion transformation and optimization objectives, and stitching/retargeting modules implemented with small MLPs.
What are the application scenarios for Live Portrait?
Applications include self-reenactment, cross-reenactment, animating still images, portrait video editing, eye and lip retargeting control, style animation, and fine-tuning for animal animation.
What is the performance of Live Portrait?
The project reports a generation speed of 12.8 ms per frame on an RTX 4090 GPU using PyTorch. The training dataset used about 69 million high-quality frames to improve quality and generalization.
How can I get more information about the Live Portrait open-source project?
Inference code and models are available at https://github.com/KwaiVGI/LivePortrait. Detailed information and experimental results are hosted on the project's GitHub page.
How can I start using Live Portrait for free?
To start using Live Portrait for free, visit the free playground at https://live-portrait.org/free-playground.

Getting Started

  1. 1 Step 1: Upload a portrait image with a clear face — Live Portrait will extract facial features from this image.
  2. 2 Step 2: Select or upload a driving video that demonstrates the facial movements you want to transfer.
  3. 3 Step 3: Click 'Animate', adjust configuration parameters if desired (or use defaults), then generate and wait for the animation to complete.

Support

email

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

Available: No

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