Segmentation
What is Segmentation?
Segmentation is an AI's ability to identify exactly which pixels in an image belong to which object: for example, precisely outlining a person separate from the background. It's what makes automatic background removal, smart rotoscoping, and selective editing possible.
At a glance
- Also known as
- Image segmentationSemantic segmentationInstance segmentationMasking
- Used for
- Rotoscoping and background removalSelective colour gradingObject isolation for VFXAI inpainting and outpaintingScene understanding
- Common tools
- Meta SAMAdobe fireflyDaVinci resolveAfter effects roto brushTopaz video AIRunway
- Related terms
- MaskingRotoscopingInpaintingOptical flowObject persistence
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How it compares
Object detection identifies what objects are present in an image and draws bounding boxes around them, but does not determine the exact pixel boundary of each object. Segmentation goes further, producing a precise pixel-level mask. For many VFX applications, bounding boxes are insufficiently precise: you need a clean, frame-accurate mask, making segmentation the appropriate tool.
Think of it like…
Segmentation is like a skilled cutter at a photo agency who, given a magazine spread, takes scissors and precisely cuts around each person, car, and tree: not just drawing a rough square around them, but following every contour exactly. The result is a stack of individually cut-out elements that can be reassembled in any combination. AI segmentation does the same thing automatically, for every frame of a video.
Pro tip
When using AI segmentation tools for rotoscoping, always review the output mask at the edges of your subject: hair, fine fabric, and motion blur are consistently the hardest areas for segmentation models, and small edge errors that are invisible at normal view can become obvious on large screens or when the composite is colour graded. Use edge refinement tools or manual touch-up passes for these areas before comping.
Types and variations
- Segmentation divides into several technical sub-types.
- Semantic segmentation assigns each pixel to a category (sky, person, car) without distinguishing between multiple instances of the same category.
- Instance segmentation identifies and separately masks each distinct object instance: distinguishing person A from person B.
- Panoptic segmentation combines both approaches, labelling every pixel with both a category and an instance ID.
- Video object segmentation tracks a specified object across frames over time.
- Promptable segmentation (as in SAM) allows users to interactively specify what to segment without category pretraining.
- Each sub-type has distinct applications and accuracy characteristics in production workflows.
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Try MorphicCommon use cases
- Segmentation is used extensively across visual effects and post-production.
- In compositing, it enables precise subject extraction for green-screen replacement and invisible set extension.
- In colour grading, it allows selective adjustments to skin, sky, or clothing without manual masking.
- In AI generation workflows, it identifies regions for targeted inpainting or style transfer.
- In virtual production monitoring, real-time segmentation can detect subject position and drive live augmented reality elements.
- In documentary and news production, automatic background replacement using AI segmentation allows field reporters to composite into studio environments without a physical green screen.
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FAQs
SAM is a promptable segmentation model released by Meta AI in 2023, trained on over a billion mask annotations. Its significance lies in its generality: rather than being trained to segment specific categories, SAM can segment almost any object in any image based on a point click, bounding box, or text prompt. This makes it a versatile foundation for building custom segmentation tools.
Fine hair, fur, semi-transparent fabric, and transparent or reflective surfaces remain difficult for all segmentation models. While accuracy has improved dramatically, these cases still typically require manual refinement in professional production. Combining AI segmentation with dedicated matting algorithms (which better handle soft, semi-transparent edges) gives the best results.
Yes: lighter, optimised segmentation models run in real time on modern GPUs and even on CPU in some cases. Real-time segmentation is used in video conferencing background removal, live broadcast effects, and increasingly in on-set virtual production monitoring tools.
Manual rotoscoping involves an artist hand-drawing frame-by-frame masks around subjects: extremely time-consuming but capable of perfect accuracy. AI segmentation produces good-to-excellent results automatically in seconds, but typically requires a review and correction pass. For most productions, AI segmentation now provides the base mask that an artist then refines rather than painting from scratch.
Segmentation identifies the specific region to be inpainted: for example, isolating an unwanted object for removal. The segmentation mask is then passed to an inpainting model which fills the masked region with contextually appropriate content. Accurate segmentation masks are directly correlated with cleaner inpainting results, as imprecise masks create visible boundary artefacts.
Single-frame segmentation treats each image independently. Temporal video segmentation maintains consistent object identity across frames, tracking how a mask evolves as the subject moves. This requires the model to correlate features between frames, resist drift, and handle occlusions cleanly: significantly harder than per-frame processing and more important for professional compositing use.
