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Green-screen keying is hardest where a simple mask is least useful: around flyaway hair, motion blur, translucent fabric and soft edges. CorridorKey is an AI-assisted tool for extracting a foreground and its transparency from green- or blue-screen footage. Rather than generating replacement imagery, it estimates the foreground color and alpha so an artist can continue compositing in a conventional VFX workflow.
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What CorridorKey does—and what “AI” means here
Ordinary chroma keying removes pixels associated with a screen color. But a pixel at the edge of a moving subject may combine the subject, the screen and motion blur; treating it as simply foreground or background can leave a jagged edge or a colored fringe. CorridorKey uses a neural network for foreground separation and matting: it predicts both a foreground image and transparency, aiming to preserve details that a hard mask can discard.
That makes it machine-learning software, but not generative AI in the usual image-making sense. Its stated purpose is to estimate how the photographed subject and screen color mix, not to invent a new scene or replace the filmed subject. The project describes its approach as physically accurate unmixing; that is the project’s characterization, not an independently verified guarantee. Its creator, Niko Pueringer of Corridor Digital, has presented the project as an artist-made tool for a tedious compositing problem. Hackaday’s March 18, 2026 coverage explains the motivation and the difficulty of semi-transparent edges.
Why its output is more than a mask
A binary mask labels a pixel as either subject or background. An alpha matte can instead assign partial transparency, which is essential for such details as hair, motion blur and translucent material. CorridorKey’s README says it aims to produce a clean, linear alpha channel alongside straight, un-multiplied foreground color, with screen-color despill. The repository also documents 16-bit and 32-bit linear-float EXR input and output. These are project-described capabilities, not a promise that every shot will key cleanly.
Premultiplication matters when bringing the result into a compositor. In premultiplied color, RGB values have already been multiplied by alpha; straight color stores the foreground color separately. If a compositor interprets one as the other, edges can show dark or colored halos. Check the footage and output interpretation in the destination application rather than assuming a good matte alone will prevent edge artifacts.
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Features and practical limits
The CorridorKey repository documents green- and blue-screen processing, automatic screen-color detection, and explicit green or blue selection. It lists a native inference size of 2048×2048 and says the system is intended to handle 4K plates by scaling; that is not the same as native 4K inference or an independently measured 4K performance result. Morphological cleanup can help remove tracking markers and small background remnants.
Optional Alpha Hint workflows can guide the model about the region to treat as foreground. The project lists BiRefNet, GVM, VideoMaMa and user-provided hints as possible routes. A hint might come from an editor, a rough mask or another method. It can still take artist judgment to make a useful guide, refine an edge, fix spill or clean up a difficult frame.
There is also a backend distinction: the repository says its Torch path supports green and blue, while its Apple Silicon MLX path currently supports green screens until a blue MLX checkpoint is available. If automatic screen detection gives a poor result, try setting the screen color explicitly; the README describes `green` and `blue` options. Consult the current README for the exact interface and flags, which may change as the project evolves.
Hardware: start with the core workflow
Hardware figures in the repository are project guidance, not independent benchmarks or guaranteed minimums. The developer reports that the original development system used an NVIDIA RTX Pro 6000 with 96 GB of VRAM, and says the current build is expected to work with roughly 6–8 GB of VRAM. Actual needs depend on the model, resolution and backend. The project also reports that most M1-or-newer Apple Silicon Macs may run it, and lists NVIDIA Windows acceleration with drivers supporting CUDA 12.8 or newer. It lists RX 7000- and RX 9000-series AMD support on Linux through ROCm, with Windows ROCm described as experimental.
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Optional GVM and VideoMaMa hint-generation components are a separate, much heavier proposition: the repository says they can require roughly 80 GB or more of VRAM. If you do not have that hardware, begin with CorridorKey’s core workflow, BiRefNet or a user-supplied hint rather than installing every optional model. The README also warns that outdated CUDA support on Windows can lead to CPU fallback, which may be dramatically slower.
Install the original project
The original repository uses uv to manage Python and dependencies; it says uv can install Python 3.10 or newer when needed. Use the project’s current instructions for the latest scripts and backend details.
Windows
- Clone or download the CorridorKey repository.
- Run
Install_CorridorKey_Windows.batand allow it to installuv, dependencies and the CorridorKey model. - If a newly installed
uvcommand is not recognized, reopen the terminal. Install optional GVM or VideoMaMa scripts only if you have the hardware to support them.
Linux or macOS
- Clone or download the repository and open a terminal in its directory.
- Run the supplied
Install_CorridorKey_Linux_Mac.shscript.
Manual setup and acceleration
The repository documents this manual setup route:
curl -LsSf https://astral.sh/uv/install.sh | sh
uv syncFor optional backend-specific dependencies, it lists:
uv sync --extra cuda
uv sync --extra mlxThe MLX option is intended for Apple Silicon. Because installation details and scripts can change, check the repository README before running commands or troubleshooting a setup.
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Choose an interface that fits your workflow
The original project provides scripts and points users to EZ-CorridorKey, an artist-oriented interface. For editors who want a host integration or a more packaged workflow, a separate project, CorridorKey-Runtime, documents a command-line runtime, a Tauri desktop GUI and an OFX plugin for DaVinci Resolve and Foundry Nuke. Its documented public builds target Windows with NVIDIA RTX and macOS with Apple Silicon. The runtime documentation does not list Adobe After Effects or Premiere as supported public plugin surfaces; it says host validation is not complete.
The runtime repository gives these example commands for its CLI:
corridorkey doctor
corridorkey process input.mp4 output.mp4
corridorkey process input.mp4 output.mp4 --preset max
corridorkey process input.mp4 output.mp4 --alpha-hint hint.mp4It also documents --json for newline-delimited event output in automation. On its Windows portable runtime bundle, the executable is ck-engine.exe. These commands belong to the separate runtime project; they are not a universal command line for the original repository.
A website called CorridorKey Studio presents a cloud interface for importing footage and generating mattes on cloud GPUs, and says local GPU mode is coming back. Its page does not establish its relationship to the original repository, nor does it make data retention, privacy, processing limits or commercial terms clear. Treat it as a separate service to evaluate, not as a confirmed official Corridor Digital offering. Uploading footage to a cloud service also differs materially from processing it locally.
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When it is—and is not—a good fit
CorridorKey is most relevant when the shot was filmed against a reasonably controlled green or blue screen and the main difficulty is edge quality rather than creating new imagery. It may be worth trying if conventional keying is struggling with hair, motion blur or soft transparency and you can inspect the result in a compositor.
It is not a substitute for a usable plate or all-purpose rotoscoping. A badly lit, wrinkled or reflective screen, substantial occlusion, changing light or a subject close in color to the screen can still make extraction difficult. A model cannot recover background information that was never captured, and its output may need a better hint, matte cleanup, despill or other compositing work. Linear EXR and alpha settings also need to be handled correctly in the host application.
Commercial use: processing footage is not reselling the tool
The project’s license distinguishes using CorridorKey to process commercial video from commercializing CorridorKey itself. It permits commercial video processing under its stated conditions, but prohibits repackaging or redistributing the tool as a standalone product, paid inference or API access, and commercial software or inference-service integration without a separate written agreement. It also requires attribution using the name “CorridorKey” and says publicly released derivatives must retain the same or a substantially equivalent license.
Those terms are not interchangeable with a blanket commercial software license. For a production company using the tool on a project, read the license; for a product, hosted service or integration, obtain legal advice and any required written agreement. The license includes its own disclaimer about enforceability and recommends independent legal advice on scope or applicability.
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The interesting distinction is not that a machine learning model removes the need for an artist. CorridorKey targets a narrow, labor-intensive stage—separating difficult screen edges—so the artist can spend less time on repetitive extraction and retain control over the hint, color treatment, cleanup and final composite. That is a meaningful use of AI for a VFX workflow, provided users treat it as an evolving tool with documented constraints rather than a one-click promise.
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