Real-time face swapping for live video and streaming
DeepFaceLive is an open-source, real-time face-swapping app for live video and streaming that runs locally on Windows and leverages user GPUs. It’s aimed at content creators, streamers, and VFX hobbyists who need low-latency face replacement and mask-based blending, and it’s free to use from the GitHub repository (no commercial hosted tiers), though GPU and setup complexity raise the bar for non-technical users.
DeepFaceLive is a real-time, local face-swapping tool that applies live facial replacements, tracking, and blending to webcam or video sources. It performs dense face alignment, mask-based blending, and target-to-source replacement for streaming, virtual production, and privacy use-cases. The key differentiator is its live, low-latency pipeline that runs on consumer NVIDIA GPUs and integrates with OBS via virtual camera or NDIs for real-time streaming. Built and distributed on GitHub, DeepFaceLive is free to download and run, but requires capable GPU hardware and some setup knowledge for best results in the AI Avatars & Video category.
DeepFaceLive is an open-source, real-time face-swapping application originally released and maintained via a public GitHub repository by the community around the iperov project. Launched in 2019–2020 timeframe (project activity and forks accelerated around 2020), DeepFaceLive positioned itself as a desktop tool for live swapping and facial reenactment that emphasizes local processing rather than cloud services. Its core value proposition is to provide low-latency face replacement suitable for livestreams, virtual cameras, and experimental VFX without sending video to external servers. The project attracts developers, hobbyists, and streamers who need a privacy-respecting, modifiable tool for swapping faces and testing models in real time.
DeepFaceLive’s feature set centers on live inference and flexible input/output. It supports multiple face models (SRCNN/DFD-like training outputs and user-trained faces exported from popular face-swap training tools), real-time face tracking with face landmarks and alignment, and mask-based blending controls to limit swaps to specific facial regions. The app exposes configurable parameters for color correction, histogram matching, and seamless clone blending to reduce artifacts. For live workflows, DeepFaceLive offers virtual camera output and NDI support so the swapped feed can be consumed by OBS, XSplit, or any NDI-compatible software. The tool also supports batching frames for offline renders, switching between multiple source/target pairs, and running on both RTX and older CUDA-capable NVIDIA cards with adjustable resolution and FPS caps.
Pricing for DeepFaceLive is straightforward because the software is distributed free on GitHub under community licensing: there is no paid hosted tier or subscription sold by the project maintainers. The repository and releases are free to download; users must provide their own GPU-equipped Windows machine (or compatible Linux builds via community forks) and, in many workflows, export trained face models from separate training tools which may have their own costs. Third-party GUIs, model packs, or training services can be paid, but core DeepFaceLive functionality has no official commercial pricing. Costs you’ll incur are hardware (NVIDIA GPU recommended), optional paid training compute/time, and any premium plugins or commercial overlays integrated into your streaming stack.
Real-world users include livestreamers replacing faces on camera, VFX artists testing composite results live, and privacy-focused presenters masking identity in broadcasts. For example, a Twitch streamer uses DeepFaceLive with OBS to produce a face-swapped persona at 30–60 FPS for audience engagement, while a VFX editor uses it to preview substitutions on reference footage before full offline renders. Two concrete job-title/use-case combos: a streamer (Twitch/YouTube) using it to increase engagement with a live face character, and a VFX compositor using it to preview face replacements on set. Compared with commercial, cloud-hosted avatar services, DeepFaceLive’s local, GitHub-distributed approach prioritizes privacy and modifiability over one-click cloud convenience.
Three capabilities that set DeepFaceLive apart from its nearest competitors.
Current tiers and what you get at each price point. Verified against the vendor's pricing page.
| Plan | Price | What you get | Best for |
|---|---|---|---|
| Free | Free | Full codebase free; requires local GPU, manual setup and model training | Developers and hobbyists with GPUs |
| Community Builds | Free | Prebuilt binaries and community plugins; support via Discord/GitHub | Streamers wanting quicker setup but DIY-minded |
| Third‑party paid services | Varies | Paid model packs/training or hosted inference sold by others | Users wanting turnkey models without local training |
Choose DeepFaceLive over Avatarify if you need local GPU-driven NDI/virtual-camera output and detailed blending controls.