A. Basics & Definitions

1. What is Meta SAM 3?
Meta SAM 3 (Segment Anything Model 3) is Meta’s unified vision foundation model that can detect, segment, and track objects in images and videos using both text and visual prompts.

2. How is SAM 3 different from the original SAM?
SAM 1 mainly segmented a single object per visual prompt in images, while SAM 3 can find all instances of a concept from text or exemplars and track them through video, in addition to classic click-based segmentation.

3. When was Meta SAM 3 released?
SAM 3 was officially announced on November 19, 2025 alongside SAM 3D.

4. What does “Segment Anything with Concepts” mean?
It means SAM 3 can segment all objects that match a concept, like “red cars” or “goalkeepers,” instead of only segmenting something you click on. 

5. Is Meta SAM 3 open source?
Yes. Meta open-sourced SAM 3’s code, checkpoints, and the SA-Co benchmark under the SAM license, which is more restrictive than standard permissive licenses but still allows wide research and some commercial use.

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B. Capabilities & Use Cases

6. What can I actually do with SAM 3?
You can use SAM 3 to isolate objects, edit regions, blur or highlight subjects, auto-label datasets, and build apps that understand scenes in images and video (sports analysis, robotics, AR, etc.).

7. Does SAM 3 work on videos as well as images?
Yes. SAM 3 works on both images and videos, and can track segmented instances across frames using the same prompt.

8. Can SAM 3 handle text prompts like “all blue cars”?
Yes, that’s one of its main features: SAM 3 accepts short noun-phrase text prompts for “open vocabulary” concept segmentation.

9. Does SAM 3 still support point and box clicks like older SAM?
Absolutely. It supports Promptable Visual Segmentation (PVS) with points, boxes, and masks, plus the new text and exemplar prompts.

10. What real-world apps are people building with SAM 3?
Common ones are photo/video editors, privacy filters (blurring faces or plates), automatic annotation tools, robot perception, and sports & traffic analytics.

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C. Prompting & Interaction

11. What is a “text prompt” in SAM 3?
A text prompt is a short phrase like “striped cat” or “yellow school bus” that tells SAM 3 which concept to find and segment.

12. What are exemplar prompts?
Exemplar prompts are example regions (usually boxes/masks) around one instance you care about; SAM 3 uses them to find all similar-looking instances in the scene or video. 

13. What is Promptable Concept Segmentation (PCS)?
PCS is SAM 3’s ability to detect, segment, and track all instances of a concept given text or exemplars, instead of a single object picked by clicks.

14. What is Promptable Visual Segmentation (PVS)?
PVS is the classic SAM behavior: you give points, boxes, or masks, and the model segments the specific object instance you indicated.

15. Can I mix text prompts and clicks together?
Yes. SAM 3 supports hybrid prompts, so you can start with text (e.g., “players”) and then refine with clicks to include or exclude specific people or objects.

16. Does SAM 3 understand long natural language instructions?
By default SAM 3 expects short phrases, but research like SAM3-I shows how it can be extended to follow longer, instruction-style prompts through extra reasoning layers.

17. How accurate are the masks from a few clicks?
On Meta’s benchmarks, SAM 3 reaches state-of-the-art accuracy on both text and visual segmentation tasks, and usually needs just a few corrective clicks to refine tricky boundaries.

18. Can SAM 3 track objects that go off-screen and come back?
It can track instances through typical motions and occlusions within a clip; if an object disappears for a long time or drastically changes appearance, you may need an extra prompt when it reappears.

19. Does SAM 3 handle multiple different concepts at once?
Yes, you can run multiple text prompts (like “cars”, “bicycles”, “pedestrians”) and get separate masks and IDs for each concept, though each extra concept adds compute cost.

20. How does SAM 3 deal with overlapping objects?
SAM 3 predicts instance masks, so even if objects overlap, it tries to assign each pixel to the correct instance; you can refine overlaps by adding positive or negative clicks.

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D. Architecture, Datasets & Performance

21. What dataset is SAM 3 trained on?
SAM 3 is powered by SA-Co (Segment Anything with Concepts), a huge dataset with ~5.2M images, 52.5K videos, ~1.4B masks and over 4M unique noun phrases linked to masks via a large ontology.

22. How much better is SAM 3 than previous systems?
On SA-Co’s Promptable Concept Segmentation benchmarks, SAM 3 delivers roughly 2× the accuracy of strong open-vocabulary baselines while also improving SAM 2-style visual segmentation.

23. What is special about the SAM 3 architecture?
SAM 3 is a unified, promptable vision-language model with a shared backbone, a “presence head” that predicts whether a concept exists, and decoders that output masks and tracks for that concept.

24. Does SAM 3 use the same encoder as SAM 2?
It’s based on a similar transformer-style vision backbone but extended so language and exemplars are tightly fused, making it capable of concept-level reasoning rather than only point-based masks.

25. How big is SAM 3 compared to SAM 2?
Meta hasn’t only focused on parameter count; they emphasize the new architecture + dataset as the main reason for the performance jump. In practical terms, you should expect SAM 3 to be heavier than SAM 2 but still efficient enough for cloud deployment.

26. Is SAM 3 real-time?
On strong GPUs it can approach near-real-time for moderate resolutions, but most tutorials treat SAM 3 as an offline or batch processor, especially for multi-concept video runs. 

27. Does SAM 3 support 3D directly?
SAM 3 itself is 2D image segmentation/ video segmentation segmentation, but it’s designed to work together with SAM 3D, which reconstructs 3D meshes from a single image using SAM-style prompts. 

28. How does SAM 3 compare with YOLO or other detectors?
YOLO and similar detectors are optimized for fast, fixed-class detection, while SAM 3 focuses on open-vocabulary, high-quality segmentation; many workflows use YOLO for real-time detection and SAM 3 for offline, high-quality masks and labels.

29. Can SAM 3 be fine-tuned?
Yes. Meta’s GitHub repo exposes fine-tuning pipelines, and frameworks like Ultralytics and Datature add simple APIs for adapting SAM 3 to domain-specific data. 

30. Is there a research paper on SAM 3?
Yes, the paper “SAM 3: Segment Anything with Concepts” is available on arXiv and on Meta’s research page, describing the architecture, SA-Co dataset, and benchmark results in detail.

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E. SAM 3D & 3D Workflows

31. What is SAM 3D?
SAM 3D is a related Meta system for single-image 3D reconstruction, turning images into textured 3D meshes of both general objects and full human bodies.

32. How is SAM 3D connected to SAM 3?
You can use SAM 3 to segment the object you care about, then feed that mask or region into SAM 3D to get a 3D object or body mesh—going from concept to 3D with minimal clicks.

33. What is the Momentum Human Rig (MHR)?
MHR is Meta’s parametric 3D mesh format for human bodies that separates skeletal structure from surface shape, making SAM 3D Body’s meshes easier to animate and interpret.

34. What’s the difference between SAM 3D Objects and SAM 3D Body?
SAM 3D Objects reconstructs generic objects and scenes, while SAM 3D Body specializes in full-body human meshes with detailed hand and foot pose using MHR. 

35. What formats does SAM 3D output?
Many integrations export SAM 3D results as standard GLB/GLTF meshes, which can be imported into engines like Unity, Unreal, or Blender.

36. What are good use cases for SAM 3D?
Popular uses include avatars, fitness apps, AR try-on, game assets, and rapid 3D prototyping from reference photos.

37. Are SAM 3D and MHR open source?
Yes. Meta open-sourced both SAM 3D Body and the Momentum Human Rig, making them available to use and extend under their published licenses.

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F. Access, Pricing & Credits

38. Is SAM 3 free to try?
Yes. You can try SAM 3 and SAM 3D for free on Meta’s Segment Anything Playground, which is browser-based and doesn’t require installation. 

39. Does Meta sell official “SAM 3 credits”?
No. Meta releases the models and demos but does not sell an official credit system. Credits usually come from third-party platforms that host SAM 3 in the cloud.

40. How do hosted platforms price SAM 3 usage?
Most platforms use credit-based or per-second pricing for example, fixed monthly credits in Basic/Pro/Max plans, or around a few thousandths of a dollar per second of video for SAM 3 segmentation.

41. What’s the difference between credits and monthly plans?
Credits measure usage units (images, seconds of video, 3D jobs), while monthly plans bundle a fixed number of credits with extra perks like faster processing and better support.

42. Can I self-host SAM 3 instead of paying for credits?
Yes. You can run SAM 3 from Meta’s GitHub repo or from frameworks like Ultralytics on your own GPU infrastructure; then your cost is mainly GPU time + storage, not per-request credits.

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G. Integration & Developer Questions

43. How do I run SAM 3 in Python?
Install a supported library (e.g., Meta’s repo or Ultralytics), load the SAM 3 checkpoint, feed an image plus prompts, and read back the mask tensors; most docs include ready-to-run notebook examples.

44. Is SAM 3 available through Ultralytics or other toolkits?
Yes. Ultralytics, Datature, Roboflow and others have integrated SAM 3, exposing text prompts, exemplar prompts, and video tracking via simple APIs and UIs.

45. Can I use SAM 3 inside a web app?
You can either call a cloud API that wraps SAM 3, or run SAM 3 on your own backend server and expose an endpoint to your frontend; Meta's Playground shows what a browser UI can look like.

46. Does SAM 3 work with other models like Stable Diffusion or video generators?
Yes. Many pipelines use SAM 3 masks to control diffusion models, apply local edits, or keep characters consistent between frames by segmenting them first and then feeding that structure to generative models.

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H. Limitations, Safety & Future

47. What are the main limitations of SAM 3?
SAM 3 can struggle with extreme occlusions, tiny objects, or very ambiguous prompts, and it is not designed to be a drop-in real-time detector on low-power devices.

48. Are there privacy concerns when using online SAM 3 tools?
Yes. When you upload images or videos to a hosted SAM 3 service, they’re processed on that provider’s servers under their privacy policy. Sensitive or confidential content is safer with self-hosting or trusted private clouds.

49. How will SAM 3 evolve in the future?
Research directions include better instruction following (SAM3-I), domain-specialized variants (medical, industrial), and tighter integration with 3D models so that text prompts can go straight to 3D-aware segmentation and reconstruction. 

50. Where can I learn more about Meta SAM 3?
The best starting points are Meta’s official SAM 3 page and blog, the SAM 3 arXiv paper, and hands-on tutorials from Ultralytics, Datacamp, Roboflow, and other computer-vision blogs.