Meta SAM 3 Video Segmentation Open Vocabulary Object Tracking in Any Clip

Meta SAM 3 Video Segmentation lets you describe what you care about once then automatically follows and masks every matching object across every frame of your video.

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Meta SAM 3 Video Segmentation – Open-Vocabulary Tracking for Any Object

Meta SAM 3 Video Segmentation takes the “segment anything” idea from still images and stretches it across time. Instead of just cutting out one object in one frame, SAM 3 can detect, segment, and track every instance of a concept across an entire video clip, using text prompts, exemplar regions, or simple clicks.

“ Think of it as: Tell me what you care about in this video – SAM 3 will follow it for you.”

1. What is Meta SAM 3 Video Segmentation?

Video segmentation in SAM 3 is built around a task called Promptable Concept Segmentation (PCS):

  • You provide a short text phrase (e.g., “players in red jerseys”, “white delivery vans”)
    or

  • An exemplar box/mask around an object you care about

…and SAM 3 will:

  1. Find all matching objects in the first frames,

  2. Create instance masks and IDs for each one,

  3. Track those instances across the rest of the clip.

It combines three things in one model:

  • Detection (find objects that match your concept)

  • Segmentation (pixel-accurate masks)

  • Tracking (consistent IDs over time)

Earlier SAM versions (SAM 1, SAM 2) could segment and track via clicks. SAM 3 adds text + exemplar concept understanding, which is what makes its video segmentation “open-vocabulary.”

2. Types of Prompts for Video Segmentation

SAM 3 video segmentation supports the same prompt family as for images, but now applied to a sequence.

2.1 Text prompts – segment by meaning

You can give short noun-phrases like:

  • “football players”

  • “cars on the road”

  • “pedestrians on the sidewalk”

  • “blue helmets”

SAM 3 will try to find every instance that matches this concept in the video and track them frame by frame.

Use text prompts when:

  • The concept is easy to describe in words.

  • You want “all of type X” rather than only a single object.

2.2 Exemplar prompts – segment “things like this”

Exemplar prompts are visual examples:

  • Draw a box around one player, car, or object in a frame.

  • SAM 3 treats that region as: “Find things that look like this.”

You can add:

  • Positive exemplars – objects that should be included.

  • Negative exemplars – look-alikes that should be excluded (e.g., referees vs. players).

Use exemplars when:

  • The category is hard to name (“this specific logo”, “this type of vehicle”).

  • There are multiple similar classes and you want just one of them.

2.3 Visual prompts (clicks, boxes, masks) – precise instance control

SAM 3 still supports Promptable Visual Segmentation (PVS):

  • Points / clicks on an object in a frame

  • Boxes around a single instance

  • Existing masks you want to refine

This is useful when:

  • You care about one hero object (e.g., a main character).

  • You want to fine-tune boundaries after a text/exemplar pass.

You can:

  1. Start with text or exemplar to get all instances.

  2. Then switch to PVS-style clicks to refine a specific instance’s mask over time.

3. How SAM 3 Tracks Objects Across Video

Under the hood, SAM 3 video segmentation works roughly like this:

  1. Backbone encodes frames

    • A transformer-based vision backbone processes each frame into rich features.

  2. Prompt encoding

    • Text → text encoder

    • Exemplars / clicks → visual prompt encoders

  3. Concept presence + instance discovery

    • A presence head predicts whether the concept is present in the clip.

    • Detection heads find candidate regions that match the prompt.

  4. Instance segmentation & IDs

    • SAM 3 outputs instance masks for matching objects.

    • Each instance gets a stable ID that is tracked over subsequent frames.

  5. Temporal consistency

    • The model uses temporal features and internal memory (inspired by SAM 2’s streaming behavior) to maintain consistency, even when:

      • The camera moves

      • Objects partially occlude each other

      • Lighting or scale changes

The result: you can scrub through the video and see each object’s mask move smoothly with the object.

4. Typical Use Cases for SAM 3 Video Segmentation

4.1 Sports video analysis

  • Track all players of a certain team using a text prompt like “players in red jerseys”.

  • Separate referees, goalkeepers, or specific roles with text or exemplars.

  • Use instance trajectories for heat maps, speed estimation, or tactical analysis.

4.2 Social media & content editing

  • Automatically isolate a subject (a dancer, a car, a product) across an entire clip.

  • Apply filters, color grading, or blurs only to that subject or the background.

  • Create stylized edits by feeding masks into other video tools (e.g., cartoon effect, AI style transfer).

4.3 Surveillance & traffic

  • Track vehicles, pedestrians, or bikes through CCTV footage.

  • Count how many objects of each type pass through a region.

  • Analyze behavior patterns (e.g., jaywalking, lane violations) based on segmentation + tracks.

4.4 Robotics & autonomous systems

  • Use SAM 3 to turn video streams into object-wise maps of the environment.

  • Track obstacles and important objects over time, not just per frame.

  • Combine with depth or LiDAR for richer scene understanding.

4.5 Data labeling for video models

  • Quickly generate ground-truth masks across tens or hundreds of frames with a single prompt.

  • Use SAM 3 output as training data for lighter, real-time models (like small segmenters or detectors) that run on edge devices.

5. How Video Credits and Costs Usually Work

SAM 3 itself doesn’t come with official pricing, but hosted platforms that offer SAM 3 video segmentation often use:

  • Per-second pricing – e.g., small fraction of a dollar per second of processed video.

  • Or credit based usage – each second/frame uses a certain number of credits.

Typical pattern:

  • Short clips (5–15 seconds) → very cheap per clip.

  • Longer clips (minutes) → better handled with a Pro or Max plan with lots of credits.

If you’re building a pricing page for “Meta SAM 3 Video Segmentation,” you can re-use the Basic / Pro / Max credit plans you already defined and explain that video jobs consume credits based on duration and resolution.

6. Getting Started with Meta SAM 3 Video Segmentation

6.1 No-code: Segment Anything Playground

Meta’s Segment Anything Playground is the easiest place to try SAM 3 on video:

  1. Upload a short video clip.

  2. Enter a text prompt like “people” or “cars”.

  3. Add exemplar boxes or clicks if needed.

  4. Play the clip and watch masks & IDs follow each object.

This is perfect for demos or understanding how prompts affect tracking.

6.2 Low-code: Hosted tools & APIs

Several platforms wrap SAM 3 video segmentation into:

  • A web UI (drag-and-drop video, choose prompts).

  • An API (send video + prompt, get back masks or JSON with tracks).

They typically offer:

  • Free trial credits

  • Paid plans for higher volumes or longer videos

  • Options to download mask sequences, alpha videos, or JSON annotations

6.3 Full control: self-hosted SAM 3

If you want maximum control:

  1. Clone the SAM 3 repo / use a compatible library.

  2. Run the model on your own GPU server.

  3. For each video:

    • Extract frames.

    • Run SAM 3’s video pipeline with your chosen prompts.

    • Save masks per frame or convert them into tracking data.

This is ideal for companies with strict privacy rules or very large volume.

7. Best Practices & Limitations

Best practices

  • Keep prompts short and clear (“red bikes”, “goalkeepers”, “white vans”).

  • Use exemplars to tell SAM 3 “this exact style of object.”

  • For mixed scenes (crowds, traffic), consider:

Limitations

  • Very long videos may need to be chunked into shorter segments.

  • Extreme motion blur, tiny objects, or very crowded scenes can still confuse the model.

  • Tracking IDs may drift if the object leaves the frame for a long time or changes appearance drastically.

For mission-critical workflows, many teams:

  • Use SAM 3 video segmentation offline,

  • Then manually review or correct key clips,

  • Or combine SAM 3 with traditional trackers and detectors for robustness.

8. How to Present “Meta SAM 3 Video Segmentation” on Your Site

To fit with your other Meta SAM pages, you can structure your page like this:

  • H1: Meta SAM 3 Video Segmentation – Track Any Concept Across Every Frame

  • Hook: A short line like
    “Type a concept or draw a box once—Meta SAM 3 will follow it through every frame of your video.”

  • Sections:

    1. What is Meta SAM 3 Video Segmentation?

    2. Prompt Types (Text, Exemplar, Clicks)

    3. How Tracking Works

    4. Use Cases (Sports, Social Video, Surveillance, Robotics, Labeling)

    5. Pricing & Credits (tie to your Basic/Pro/Max plans)

    6. How to Get Started (Playground / API / Self-host)