DeepFaceLive — Real‑Time Face Swap & Animator

Real-time face swapping and animation — make video calls and streams more creative

Ever wished you could change your face on a livestream, in a video call, or test a funny animation in real time without complicated setups? DeepFaceLive offers a portable, zero-dependency toolset that brings face swapping and face animation to your desktop — optimized for DirectX12 and popular GPUs. It ships with a library of ready-to-use synthetic face models and features built-in modes for single-photo swaps, webcam-driven swaps, and photo animation.

This article walks through what DeepFaceLive does, who its for, how it works, and how to get started using the releases and documentation referenced below.

What It Does

DeepFaceLive is a real-time face processing application with three main capabilities:

  • Face Swap (DFM) — swap your face from a webcam or swap into faces inside videos using pre-trained face models. The project includes a public library of ready-to-use face models (examples shown in the repo documentation) that let you quickly try celebrity-style swaps and synthetic personas.
  • Face Swap (Insight) — perform swaps using a single static photo as the source, enabling quick single-photo replacements rather than requiring a trained model.
  • Face Animator — control a static face picture using a live video or camera feed. The animator maps expressions and performs real-time driving of a still face to create animated clips or streaming overlays.

“There are ready-to-use public face models included, and if you need higher quality you can train your own model with DeepFaceLab.”

Who It’s For

This project is aimed at hobbyists, streamers, meme creators, content producers, and developers experimenting with real-time face synthesis. Typical users include:

  • Streamers who want live face swapping overlays or funny persona filters for entertainment streams.
  • Content creators producing short clips, memes, or social media videos.
  • Developers and researchers wanting a DirectX12-capable, real-time face manipulation tool to prototype ideas.
  • Users who want a portable, zero-dependency application that runs without installing complex frameworks (beyond native video drivers).

Skill level: basic to intermediate. Non-developers can use pre-built releases and included models; advanced users can train custom models via the referenced DeepFaceLab project for improved results.

How It Works

At a high level, DeepFaceLive operates as a real-time DirectX12 application that performs face detection, alignment, synthesis, and compositing on each video frame. Key aspects include:

  • Real-time pipeline: Captures camera or video frames, detects faces, applies the selected model (DFM or Insight) or animator mapping, then blends the generated face back into the frame for output to a video call, stream, or recording.
  • Multiple modes: DFM (trained model swap), Insight (single-photo swap), and Face Animator (drive a static face image).
  • Hardware acceleration: Designed for DirectX12 compatible GPUs. There is an NVIDIA-specific build that runs faster on NVIDIA cards and a DirectX12 build that supports NVIDIA, AMD, and Intel GPUs.

Key technologies mentioned

  • DirectX12 (primary runtime for GPU acceleration)
  • ONNX (model interchange format — logos shown in the docs)
  • Python (logo present in docs; used in related tooling and training workflows)

If you need higher-quality trained face models, the documentation points to training workflows handled by DeepFaceLab, which is the recommended path to produce custom models for the DFM mode.

Getting Started

If you just want to try DeepFaceLive on Windows, the maintainers provide ready-to-use releases and documentation. The distribution is a portable, self-extracting folder that requires only video drivers. Follow the steps below to get a working copy running.

<!-- Download and run (Windows 10 x64) -->
# 1. Download the release (Windows 10 x64):
#    - https://disk.yandex.ru/d/7i5XTKIKVg5UUg
#    - https://mega.nz/folder/m10iELBK#Y0H6BflF9C4k_clYofC7yA

# 2. Extract the self-extracting portablle folder.
#    The release is zero-dependency and portable; you do not need to install additional runtime libraries besides up-to-date video drivers.

# 3. Run the application executable from the extracted folder.

# 4. Read the included documentation for setup, streaming, and video call integration:
#    - Windows main setup: doc/windows/main_setup.md
#    - For streaming: doc/windows/for_streaming.md
#    - For video calls: doc/windows/for_video_calls.md
#    - Using Android phone camera: doc/windows/using_android_phone_camera.md

# 5. If you want higher-quality models or to train custom faces, follow DeepFaceLab training instructions:
#    - https://github.com/iperov/DeepFaceLab

Note: The instructions above summarize the installer-free release flow described in the project documentation. If you need build instructions for Linux, consult the build info entry in the documentation.

Key Features

  • Multiple face modes: DFM trained-model swaps, Insight single-photo swaps, and a Face Animator module to drive static images.
  • Ready-to-use model library: The docs include many prebuilt public face models and examples (thumbnails and example pages are listed in the documentation).
  • Portable releases: Stand-alone, zero-dependency, self-extracting releases for Windows 10 x64.
  • DirectX12 support: Works on DirectX12-compatible GPUs; NVIDIA-specific build available for improved performance on NVIDIA cards.
  • Streaming & calling integration: Guides available for streaming setups and for using the tool in video calls.
  • Community communication: Official Discord and Chinese QQ group links are provided for discussion and model sharing.

System requirements (as provided): any DirectX12 compatible graphics card (recommended RTX 2070+ / Radeon RX 5700 XT+), modern CPU with AVX, 4GB RAM + 32GB+ paging file, Windows 10.

Why It’s Worth Trying

DeepFaceLive is a practical choice if you want a fast path to real-time face swapping without building from source or installing heavy dependencies. The portable release model and included face library let you experiment immediately. The Face Animator opens up playful possibilities for memes and short form content, and the insight-mode enables quick single-photo swaps for less-complex use cases.

Community and support options are provided directly in the documentation:

  • Official Discord: discord.gg/rxa7h9M6rH (English / Russian)
  • Chinese QQ group: QQ群124500433 (for Chinese-language discussions)

Model training and contribution: The project encourages users to train and share their own face models. If a model meets quality expectations it may be added to the public library via the Discord community.

GitHub Link

The project documentation references the DeepFaceLab training repository for custom model creation: https://github.com/iperov/DeepFaceLab. Released builds for DeepFaceLive are available via the downloads listed in the Releases section above (Yandex and Mega links). The official project resources and documentation pages linked in the repo are the best place to get the most up-to-date releases and usage guides.

Important: The repository text provided here does not include explicit GitHub star or contributor counts. For community metrics such as stars, contributors, and issue activity, please consult the project’s GitHub or release pages directly.

Final Thoughts

DeepFaceLive is a compact, pragmatic toolkit for real-time face swapping and animation. It prioritizes portability and quick experimentation through prebuilt models and ready-to-run Windows releases. While high-quality, production-ready results typically require custom-trained models (for which DeepFaceLab is recommended), the out-of-the-box features are excellent for hobbyists, streamers, and creators who want to prototype fun content quickly.

Whether youre trying a silly face swap during a stream, animating a static portrait for a short clip, or experimenting with custom training pipelines, the documentation and community links provided in the project are a good starting point. If youre interested in contributing, training and sharing models on Discord is the suggested path — and you can support the project via the donation channels listed in the docs.

Find the downloads, documentation, and communication links in the repository resources below and start experimenting.