SAM 3 Visual Prompts: From Click-to-Segment to Concept-Aware Interactions

Segment Anything Model 3 (SAM 3) is known for its powerful text and concept prompts but that doesn’t mean the classic visual prompts disappeared. In fact, SAM 3 keeps and improves the clicks, boxes, and masks style of interaction that made earlier SAM versions popular, then layers them into a much richer promptable concept segmentation (PCS) pipeline.

If you’re coming from SAM 1 or SAM 2, “visual prompts” were the main way you interacted with the model:

• Click on an object → get a mask

• Draw a box → refine the region

• Paste a rough mask → improve edges

With SAM 3 visual prompts, you can still do all of that but now inside a model that also understands text, image exemplars, hybrid prompts, and video tracking. Visual prompts become one flexible input channel among many.

In this article, we’ll walk through:

• What “visual prompts” mean in SAM 3

• How they relate to concept prompts and PCS

• Types of visual prompts (points, boxes, masks, regions)

• Workflows on images and video

• When to use visual prompts instead of text

• UI, UX, and integration ideas

• Limitations, trade-offs, and best practices

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1. What Are Visual Prompts in SAM 3?

A visual prompt is any spatial, visual hint you give the model on top of the raw image or frame—such as:

• A point (or multiple points) you click on

• A bounding box around an area of interest

• An existing mask (from a previous prediction or another model)

• A region or polygon scribble

Instead of only telling SAM 3 what you want via language (“yellow bus”), you tell it where to look, or which pixels are foreground vs background.

SAM 3 supports visual prompts as a legacy from earlier Segment Anything models, but now they can be:

• Used alone, like in SAM 1/SAM 2

• Combined with text prompts, e.g.,

  • “Person” + click on one person

  • “Car” + box around a cluster of cars

• Used to refine concept-based outputs, by cleaning up or correcting masks returned by text or hybrid prompts

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2. Why Visual Prompts Still Matter in a Text-Prompt World

Text prompts are incredibly powerful, but visual prompts still solve real problems that language alone can’t handle cleanly.

2.1 Disambiguation in crowded scenes

Example:

• Text prompt: “person with backpack”

• There are 8 people with backpacks in the image.

• You only want one of them.

You can combine a visual prompt (a click/box on the specific person) with the text concept to guide SAM 3:

• Visual prompt → this particular person here

• Text concept → the “person-with-backpack” semantics

Result: SAM 3 focuses on the right instance, instead of returning masks for all matches.

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2.2 Fine control for creative workflows

In video editing, graphic design, or VFX, artists often:

• Want pixel-perfect edges around a subject

• Need to control exactly which object is segmented

• Prefer interactive refinement over only text

Visual prompts make the process feel like painting with AI:

1. Text prompt for the general concept: “bride’s dress”

2. Visual click to choose which person (if multiple)

3. Visual corrections to fix sleeves, hair, veil edges

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2.3 “I don’t know what this is called, but I can point at it”

Sometimes, text fails because:

• You don’t know the proper name of the object

• The concept is vague (“this texture”, “this exact logo”)

• There’s no clean noun phrase

Visual prompts let you say:

“I don’t know the word, but this thing here segment that.”

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3. Types of Visual Prompts in SAM 3

While the exact API details depend on the implementation, SAM 3 visual prompts can be grouped into a few core types:

3.1 Point Prompts

The classic: single or multiple clicks on the image.

• Positive points: “this pixel belongs to the object”

• Negative points: “this pixel does not belong to the object”

Usage examples:

• One positive point on a person’s shirt

• Several positive points along a car’s body

• Negative points on the background to prevent leak

SAM-like models use these points to help the segmentation head infer the full shape.

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3.2 Box Prompts

A bounding box around a region of interest:

• Quick way to tell SAM 3 where to look

• Useful for rough localization when text is too broad

Example workflow:

1. Draw a box around a cluster of objects (e.g., a shelf with multiple products).

2. Use text: “blue cereal box” inside that box.

3. SAM 3 returns masks only inside that spatial region.

Boxes are powerful for narrowing down context, especially in cluttered images.

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3.3 Mask Prompts

You can feed an existing mask to SAM 3 as a prompt:

• A previous SAM 3 output

• A mask from another model (e.g., an instance segmentation model)

• A very rough hand-drawn mask

Why use masks as prompts?

• To refine edges, fill holes, fix jagged boundaries

• To propagate an initial mask across video frames (with tracking)

• To treat one segmented region as a “seed” to grow or shrink

SAM 3 can treat the mask as an initial guess and adjust it using its deeper features and concept awareness.

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3.4 Region or Scribble Prompts

Depending on your tool or UI, you might also support:

• Scribbles: quick strokes marking foreground vs background

• Polygon selections: manual outlines passed as prompts

These are basically more expressive variants of mask/point prompts, especially useful in annotation tools.

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4. Visual Prompts vs Text Prompts vs Hybrid Prompts in SAM 3

SAM 3 unifies three kinds of prompting:

• Visual prompts: points, boxes, masks

• Text prompts: short noun phrases, concept phrases

• Image exemplars: reference crops or images

So what’s the difference in usage?

4.1 When to use only visual prompts

Use purely visual prompts when:

• You’re doing interactive segmentation (like a Photoshop-style helper)

• You don’t care about the object category just need masks

• You want fine manual control, like in annotation tools

This feels very similar to SAM 1 / SAM 2 workflows.

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4.2 When to use only text prompts

Use only text prompts when:

• You want open-vocabulary segmentation without interaction

• You want all instances of a concept (“all red cars”, “soccer balls”)

• You’re running large-scale batch processing or automation

This is where SAM 3’s concept engine shines.

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4.3 When to use hybrid visual + text prompts

This is often the sweet spot for SAM 3:

• Text defines the semantic concept

• Visual focuses on the spatial instance or subset

Example:

• Text: “glass bottle”

• Visual: one click on the specific bottle you care about

• Result: SAM 3 segments the correct bottle, not every bottle in the scene

In complex scenes or video, hybrid prompts often produce the cleanest, most controllable results.

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5. SAM 3 Visual Prompt Workflow on Images

Let’s walk through a typical image workflow in a UI or script.

Step 1: Load Image and Model

You load the image into your tool and initialize SAM 3 (from GitHub, Hugging Face, or an integrated SDK).

Step 2: User Adds a Visual Prompt

The user interacts:

• One click on the object

• Or a box around it

• Or a rough mask

The UI converts this into prompt coordinates / masks for the model.

Step 3: Run SAM 3 with Visual Prompt

The backend sends something like:

• Image tensor

• Prompt type: point/box/mask

• Optional: text prompt

SAM 3 processes the prompt through its prompt encoder and segmentation decoder, returning:

• One or more masks

• Confidence scores

• (If text is involved) concept presence signals

Step 4: Display and Refine

The UI overlays the mask:

• User can add more points (positive or negative)

• User can adjust or erase parts of the mask

• New prompts are sent for iterative refinement

Step 5: Export

Once the user is satisfied:

• Export masks as PNGs, alpha channels, vector shapes, or polygon coordinates

• Feed them into downstream pipelines (VFX, product cutouts, data labeling, etc.)

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6. SAM 3 Visual Prompt Workflow on Video

Visual prompts are also important in video.

6.1 Initialize on a Key Frame

You start with a key frame:

1. Choose a frame where the object is clearly visible.

2. Add a visual prompt:

   • Click on the object

   • Draw a box

3. (Optionally) Add a text concept for semantic hints.

6.2 Run “First Frame” Segmentation

SAM 3 segments the object in that frame, producing:

• A high-quality mask

• An instance ID

6.3 Track Through Time

Then SAM 3’s video tracker:

• Propagates the mask across subsequent frames

• Maintains the same instance ID

• Adjusts masks as the object moves, rotates, or changes scale

6.4 Correct with Additional Visual Prompts

When tracking drifts or fails (occlusions, fast motion):

• The user can click on a frame where the mask is wrong

• Provide additional visual prompts (e.g., correction clicks)

• SAM 3 re-aligns the track from that point onward

This is extremely powerful for:

• Rotoscoping

• Object-focused video analytics

• Automated highlight generation (e.g., tracking “ball” in sports)

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7. Implementing Visual Prompts: Practical Considerations

If you’re a developer, here’s how to think about integrating SAM 3 visual prompts into your tooling.

7.1 Coordinate Systems

Your frontend UI:

• Works in pixel coordinates (x, y) relative to the displayed image.

• May be scaled, cropped, or padded compared to the model’s input.

You must:

• Convert UI coordinates → model input coordinates

• Apply any resizing / padding transformations consistently on input and mask output

7.2 Prompt Encoding

Visual prompts are usually encoded as:

• Point arrays: [x, y, label] where label is positive/negative

• Box arrays: [x1, y1, x2, y2]

• Binary masks: same spatial resolution as input (or downscaled/upscaled)

Your library or SDK (GitHub/HF) will often provide helper functions to build these prompt tensors.

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7.3 State Management for Interactive Sessions

In an ideal UI:

• Each interaction (click, box, correction) becomes a new prompt added to the “state” for that object.

• You may keep a list:

  • Points so far

  • Current mask

  • Object ID

When the user adds a new prompt, you send the full prompt history (or a summarized version) so SAM 3 can refine the segmentation more intelligently.

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7.4 Performance

Visual prompt workflows are often interactive humans are waiting for feedback:

• You want response times under ~300–500 ms for a good UX.

• That may mean:

  • Using a smaller SAM 3 variant

  • Caching features of the image so you don’t recompute the backbone every time

  • Running on GPU

A common approach:

1. Compute the image’s backbone features once.

2. For each new visual prompt, only run the prompt encoder + segmentation head.

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8. Advantages of SAM 3 Visual Prompting

8.1 Fine-Grained Control

Visual prompts give per-object, per-pixel control that pure text can’t match, especially in creative and manual workflows.

8.2 Human-in-the-loop labeling

For dataset construction:

• SAM 3 can propose masks

• Annotators use visual prompts to quickly fix them

• This drastically cuts annotation time compared to drawing masks from scratch

8.3 Backward Compatibility with Existing Tools

If you previously integrated SAM 1 / SAM 2-style interactions:

• You can upgrade to SAM 3’s open-vocabulary power

• Still keep your existing UI pattern (clicks, boxes, etc.)

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9. Limitations and Failure Modes of Visual Prompts in SAM 3

Visual prompting isn’t magic; it has some limitations.

9.1 Ambiguous Regions

If you click on a blurry, mixed region (like motion blur or transparent glass), SAM 3 may incorrectly estimate boundaries.

Mitigation:

• Place points on clear, well-defined object pixels

• Use multiple points for large or complex shapes

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9.2 Complex Overlaps

When objects overlap heavily (people in a crowd, tangled objects):

• A single click might not be enough to isolate the exact instance.

Mitigation:

• Combine text (“child in blue jacket”) + click on that specific child

• Add negative points on adjacent objects

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9.3 Domain Shift

In extremely unusual domains (e.g., medical scans, thermal images):

• Visual prompts help, but the underlying features may not generalize well.

• SAM 3 may still need domain-specific fine-tuning for high accuracy.

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9.4 Video Drift

In video:

• Even with visual prompts, tracking can drift if frames are heavily blurred, occluded, or jump-cut.

Mitigation:

• Use keyframe correction periodically add fresh visual prompts on new frames to realign tracking.

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10. Best Practices for “SAM 3 Visual Prompt” Workflows

Here are some practical guidelines to get the best results.

10.1 Use Clear Foreground Points

• Place clicks on visually distinctive parts of the object (edges, patterns, colors).

• Avoid extremely ambiguous regions like reflections or shadows.

10.2 Combine Positive and Negative Points

• Use positive points to define the object

• Use negative points to carve away background or neighboring objects

This dramatically improves mask quality.

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10.3 Use Text When the Scene is Busy

In busy scenes:

• Start with a text prompt (“yellow excavator”)

• Add a visual prompt to pick exactly which excavator you care about

You get both semantic understanding and spatial precision.

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10.4 Cache Features for Interactivity

For UI apps:

• Precompute and cache backbone features

• Only recompute the prompt-specific parts

This makes visual prompting feel instant rather than “click and wait”.

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10.5 Save Prompt Histories

If you’re building a professional tool:

• Store each object’s prompt history (points, boxes, etc.)

• Allow users to reload and re-edit objects without starting from scratch

This is also helpful if you upgrade to a new SAM version prompt histories can be re-run with improved models.

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11. Building Products Around SAM 3 Visual Prompts

Some concrete product ideas where SAM 3 visual prompts are central:

11.1 Video Editing Panel

• Timeline view with frames

• Click on an actor → create tracked mask with SAM 3

• Offer “Refine mask” button with additional prompt clicks

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11.2 Labeling/Annotation Tool

• Display images from training datasets

• Auto-suggest masks via concept prompts

• Annotators fix them via visual prompts

• Export cleaned masks + polygons

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11.3 AR/Camera App

• Live camera feed

• User taps an object → SAM 3 segments it

• Apply filters, color changes, or overlays only on that object

Visual prompts here are just taps on the screen.

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12. Visual Prompts as Part of the Bigger SAM 3 Story

SAM 3 is often described in terms of:

• Promptable Concept Segmentation (PCS)

• Open-vocabulary segmentation

• Text/image exemplars

• Tracking and SA-Co benchmark

Visual prompts are the bridge between that high-level vision and the proven, practical workflows from earlier SAM generations. They let humans:

• Intervene when text alone is not enough

• Correct and refine masks

• Align the model’s understanding with their intent

In other words, SAM 3 visual prompts are how you “steer” the model interactively one click, box, or scribble at a time.

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13. Summary: Key Takeaways About SAM 3 Visual Prompts

• Visual prompts are still first-class citizens in SAM 3: points, boxes, masks, and regions remain essential.

• They work alongside text and exemplar prompts, not instead of them.

• Use visual prompts when you need:

  • Per-instance control

  • Fine-grained edits

  • Manual correction in labeling and VFX workflows

• Use hybrid prompts (visual + text) for the best of both worlds in cluttered scenes.

• In video, visual prompts seed the tracker, and additional prompts correct drift.

• Implementations should focus on:

  • Good coordinate handling

  • Fast, interactive response times

  • Storing prompt histories for re-editing

If you’re building tools on top of SAM 3, leaning into visual prompts gives your users a feeling of direct, tactile control instead of just typing prompts and hoping for the best.