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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