Live Portrait
Efficient and Controllable Video-Driven Portrait Animation

The industry-standard open-source deep learning framework for realistic face swapping and manipulation.
FaceSwap is a leading open-source deepfake project that leverages advanced neural network architectures, specifically Autoencoders and Generative Adversarial Networks (GANs), to facilitate the swapping of faces in images and videos. As of 2026, it remains the gold standard for researchers, hobbyists, and digital artists who require granular control over the facial reconstruction process. Unlike closed-loop commercial SaaS alternatives, FaceSwap offers a modular plugin-based architecture, allowing users to select between various extraction, alignment, and training methods such as S3FD for detection and FAN for alignment. The software is written in Python and utilizes TensorFlow and Keras for its deep learning backend, supporting both NVIDIA (CUDA) and AMD (ROCm) hardware. Its market position is defined by its transparency and privacy, as all processing occurs locally on the user's hardware. While the learning curve is steep, the output quality in 2026 is unparalleled due to the integration of transformer-based encoders and advanced masking techniques (like XSeg) that allow for seamless blending even in complex lighting and occluded environments.
Allows users to swap out extraction, alignment, and training modules without rewriting the core engine.
Efficient and Controllable Video-Driven Portrait Animation
Turn 2D images and videos into immersive 3D spatial content with advanced depth-mapping AI.
High-Quality Video Generation via Cascaded Latent Diffusion Models
The ultimate AI creative lab for audio-reactive video generation and motion storytelling.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
A sophisticated masking tool where users can manually train a model to recognize facial boundaries and occlusions.
Utilizes the Face Alignment Network to identify landmarks in 3D space, ensuring stability across head turns.
Includes 'Villain' and 'RealFace' models that use Discriminators to force the Generator to create higher-fidelity textures.
Distributes training loads across multiple local GPUs using data parallelism.
Uses VGG Face or face-distance algorithms to automatically group faces and delete blurry frames.
Includes Lab-Color, Seamless Clone, and Match-Histograms for blending the swapped face with the original skin tone.
Reducing the cost of digital de-aging for independent films.
Registry Updated:2/7/2026
Mismatched lip movements in dubbed international content.
Protecting the identity of whistleblowers while maintaining emotional expression.