Deep-Live-Cam open source analysis
real time face swap and one-click video deepfake with only a single image
Project overview
⭐ 76792 · Python · Last activity on GitHub: 2025-12-15
Why it matters for engineering teams
Deep-Live-Cam addresses the challenge of real-time face swapping and video deepfake generation using only a single image, providing a practical solution for AI and machine learning engineering teams working on computer vision and generative models. It is particularly suited for roles focused on AI engineering and machine learning research where real-time processing and accuracy are critical. The project is mature and widely adopted, demonstrating reliability for experimental and prototype stages, though caution is advised for production environments requiring strict ethical considerations and high security. It may not be the right choice for teams needing fully customisable models or those prioritising explainability over performance, as it focuses on convenience and speed rather than extensive model tuning or transparency.
When to use this project
This open source tool for engineering teams is a strong choice when rapid deployment of real-time deepfake or face swap capabilities is needed, especially in research or proof-of-concept projects. Teams should consider alternatives if they require more control over model architecture or need a solution designed specifically for large-scale production environments with strict compliance requirements.
Team fit and typical use cases
AI and machine learning engineers benefit most from Deep-Live-Cam as a self hosted option for real-time face manipulation tasks. They typically use it to develop interactive applications, video editing tools, or augmented reality experiences where live video processing is essential. The project appears in products that require fast, on-the-fly video deepfake generation or face swapping, often serving as a foundation for further customisation and integration into broader AI-driven systems.
Best suited for
Topics and ecosystem
Activity and freshness
Latest commit on GitHub: 2025-12-15. Activity data is based on repeated RepoPi snapshots of the GitHub repository. It gives a quick, factual view of how alive the project is.