Meta SAM 3 Features – Everything “Segment Anything 3” Can Do

Meta SAM 3 is the third generation of Meta’s Segment Anything family: a unified vision model that can detect, segment, and track any object in images and videos using text, exemplar, and visual prompts.

Here’s a structured, feature-focused overview you can use directly on your website.

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1. Unified Model for Images and Video

Earlier versions were split:

• SAM 1 → mainly image segmentation

• SAM 2 → image + video with click/box/mask prompts

SAM 3 combines and extends these:

• One model for high-quality segmentation in images and videos

• Shared architecture that understands both spatial (image) and temporal (video) information

• Built to handle short clips and single frames with the same prompt style

This makes SAM 3 easier to integrate: you don’t need separate models for still images vs. moving content.

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2. Open-Vocabulary Text Prompts

One of the biggest features of Meta SAM 3 is open-vocabulary text prompting:

• Use short phrases like:

  • “red cars”

  • “goalkeepers in green”

  • “solar panels on roofs”

  • “blue delivery vans”

• SAM 3 finds all objects that match that concept and returns instance masks for each.

Key points:

• Not limited to a fixed label set (unlike classic detectors).

• Works for everyday scenes (streets, sports, indoor) and more niche concepts as long as they appear in the training distribution.

• Supports multiple concepts on the same image or video by running multiple prompts (e.g., cars + bikes + pedestrians).

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3. Exemplar-Based “Segment Things Like This”

SAM 3 also supports exemplar prompts – visual examples:

• Draw a box or give a mask around one object you care about.

• SAM 3 uses it as a template to find all visually similar objects.

This is powerful when:

• The thing is hard to name (a unique logo, brand-specific product, custom uniform).

• Different classes look similar and you only want one version.

• You need very fine control over which style or subtype to segment.

You can combine:

• Positive exemplars → “include objects like this”

• Negative exemplars → “exclude objects like this”

to tighten the result.

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4. Classic Click, Box & Mask Prompts (PVS)

Meta SAM 3 still supports the interactive prompts from earlier SAM versions, known as Promptable Visual Segmentation (PVS):

• Points / clicks

  • Positive clicks on the object

  • Negative clicks on background or wrong regions

• Boxes

  • Rough bounding box around the object

• Masks

  • Existing segmentation you want to refine

Use PVS when:

• You want pixel-perfect detail on a specific object.

• The concept is ambiguous and you’d rather “show” than describe.

• You’re doing interactive editing inside a UI or creative app.

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5. Promptable Concept Segmentation (PCS)

PCS is the headline feature of SAM 3:

“Given a text or exemplar prompt, find, segment, and track every instance of that concept in an image or video.”

PCS features:

• Works with text, exemplars, or both.

• Outputs instance masks + IDs (for video, IDs are stable across frames).

• Handles multiple instances automatically (all players, all buses, all trees).

Compared to SAM 2, which mainly segments “this object I clicked,” SAM 3’s PCS lets you build whole-scene understanding with a single concept prompt.

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6. State-of-the-Art Accuracy & New Datasets

To power these features, SAM 3 uses a new large-scale dataset (often referred to as SA-Co – Segment Anything with Concepts) with:

• Millions of images and short videos

• Over a billion masks

• Millions of short concept phrases (noun phrases) mapped to those masks

• Hard negatives (phrases that shouldn’t match a region)

Benefits:

• Stronger open-vocabulary performance – SAM 3 is much better at understanding real phrases like “striped shirt” or “yellow taxi.”

• 2× performance gains on Meta’s concept-segmentation benchmarks compared to older open-vocabulary methods.

• More robust behavior on busy, messy real-world scenes (crowds, streets, cluttered rooms).

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7. Integration with Meta SAM 3D (Single-Image 3D)

While SAM 3 itself is a 2D model, it’s designed to work together with SAM 3D, Meta’s single-image 3D reconstruction system:

1. Use SAM 3 to segment objects or people with text or exemplar prompts.

2. Feed the segmented region to SAM 3D Objects or SAM 3D Body.

3. Get a 3D mesh (with texture) for that object or human.

This unlocks workflows like:

• “Segment all cars in this street photo → select one → convert to 3D model.”

• “Find the main player in a sports still → reconstruct their 3D pose/body for analytics or AR.”

So SAM 3 becomes the front door for 2D→3D pipelines.

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8. Performance & Efficiency Features

Even though SAM 3 is large, it’s designed to be usable in production:

• Shared backbone for images and video (no separate models to juggle).

• Efficient heads that can reuse prompts across frames or multiple images.

• Robust tracking for short clips without needing huge GPU memory per frame.

Most real-world usage treats SAM 3 as:

• Batch/offline for heavy jobs (long videos, many prompts).

• Interactive for creators via web apps and GUIs, backed by GPU servers.

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9. Practical Use Cases for Meta SAM 3 Features

You can highlight these on your site as “What you can build with SAM 3”:

9.1 Creative & editing tools

• Auto background removal

• Region-specific effects (color, blur, glow, stylization)

• Smart subject selection for thumbnails and posters

9.2 Sports & broadcast

• Tracking all players or specific roles

• Automatic highlight extraction and overlays

• Player-wise heatmaps and trajectories

9.3 Mapping & infrastructure

• Segment roads, buildings, trees, solar panels, vehicles

• Count and measure objects in aerial images

• Help create datasets for urban planning and environment monitoring

9.4 Robotics & autonomy

• Turn camera streams into segmented maps

• Keep consistent IDs on important objects over time

• Combine with depth to understand scene layout

9.5 Dataset creation & annotation

• Automatically label huge image/video datasets

• Use open-vocabulary prompts to find rare categories

• Generate masks that train lighter models (YOLO, small segmenters) later