GitHub

Deep-Live-Cam Project Description

What is the project about?

Deep-Live-Cam is a real-time face-swapping and video deepfake application. It allows users to replace a face in a video or webcam feed with another face from a single image, with just a few clicks.

What problem does it solve?

The project provides an accessible and user-friendly way to create deepfakes, which can be used for various creative and entertainment purposes. It removes the complexity typically associated with deepfake creation, making it available to a wider audience. It also addresses ethical concerns by including built-in checks to prevent the processing of inappropriate media.

What are the features of the project?

  • Real-time face swapping: Replaces faces in live video feeds or pre-recorded videos.
  • Single-image face swap: Requires only a single image of the desired face.
  • One-click operation: Simplified user interface for ease of use.
  • Mouth Mask: Option to retain the original mouth movements for more realistic results.
  • Face Mapping: Ability to use different faces on multiple subjects simultaneously.
  • Inappropriate content check: Built-in safeguards to prevent misuse with sensitive or harmful content.
  • Multiple Execution Providers: Support for various hardware acceleration options (CUDA, CoreML, DirectML, OpenVINO).
  • Pre-built packages for Windows and Mac.

What are the technologies used in the project?

  • Python: The primary programming language.
  • pip: For managing Python packages.
  • git: For version control.
  • FFmpeg: For video processing.
  • Visual Studio 2022 Runtimes (Windows): Dependencies for Windows.
  • Deep Learning Models:
    • GFPGANv1.4 (for face restoration)
    • inswapper_128_fp16.onnx (for face swapping)
  • ONNX Runtime: For running the deep learning models.
  • CUDA Toolkit (Nvidia): For GPU acceleration on Nvidia GPUs.
  • CoreML (Apple Silicon/Legacy): For GPU acceleration on Apple devices.
  • DirectML (Windows): For GPU acceleration on Windows with compatible hardware.
  • OpenVINO™ (Intel): For optimization on Intel hardware.
  • insightface: library and models.

What are the benefits of the project?

  • Accessibility: Easy to use, even for non-technical users (especially with pre-built versions).
  • Real-time performance: Allows for live deepfake creation.
  • Creative potential: Enables various applications in content creation, entertainment, and art.
  • Ethical considerations: Includes measures to prevent misuse.
  • Hardware flexibility: Supports various hardware configurations for optimized performance.

What are the use cases of the project?

  • Content creation: Animating custom characters, creating engaging social media content.
  • Entertainment: Live shows, performances, and meme creation.
  • Virtual meetings/streaming: Using different personas in online interactions (e.g., Omegle).
  • Movie dubbing: Replacing actors' faces in movies in real-time.
  • Clothing design: Using models for clothing design.
Deep-Live-Cam screenshot