Analysis updated 2026-05-18
Animate a still photo of a person to match head movements and expressions from a video.
Create lip-synced video of a portrait by driving it with audio or a reference video.
Animate pet photos (cats, dogs) to move naturally using a motion reference.
Build a video editing tool that lets users control portrait animation interactively in a browser.
| klingairesearch/liveportrait | huggingface/trl | state-spaces/mamba | |
|---|---|---|---|
| Stars | 18,333 | 18,367 | 18,240 |
| Language | Python | Python | Python |
| Setup difficulty | hard | moderate | moderate |
| Complexity | 3/5 | 4/5 | 4/5 |
| Audience | developer | researcher | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires PyTorch installation, pre-trained model downloads, and GPU/CUDA for reasonable performance, CPU-only is very slow.
LivePortrait is a Python research project for animating portrait images using AI. Given a still photo of a face, the system can make it move in a lifelike way by using a driving video or image as a reference for the motion, for example, making a portrait smile, turn its head, or lip-sync to audio. The project comes from researchers at Kuaishou Technology and has been adopted in video platforms including Kuaishou, Douyin, and WeChat Channels. It supports animating both human faces and animals (such as cats and dogs). The system includes controls for stitching (blending the animated result back with the original image naturally) and retargeting (mapping motion from a driver source onto the portrait). It can be run locally on Linux, Windows, or macOS with Apple Silicon, and the repository includes a browser-based interface for interactive use. A one-click installer for Windows is available. Modes include driving from a video, driving from an image, and editing a portrait video. A hosted online demo is also available. The code and associated neural network models are provided for research and inference use.
AI system that animates still portrait photos to move naturally, driven by a video or image reference. Works on faces and animals with local or browser-based control.
Mainly Python. The stack also includes Python, PyTorch, Deep Learning.
License could not be detected automatically. Check the repository's LICENSE file before use.
Setup difficulty is rated hard, with roughly 1h+ to a first successful run.
Mainly developer.
This repo across BitVibe Labs
Verify against the repo before relying on details.