SAM 3 Video: AI-Powered Object Segmentation and Tracking

Edited by Segmind Team on November 29, 2025.

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What is SAM 3 Video?

SAM 3 Video is the latest foundation model by Meta, designed for prompt-based video segmentation and intelligent object tracking. It is structured on the Segment Anything Model (SAM) framework, an advanced version of the next-generation AI that is capable of seamlessly detecting, isolating, and following objects across frames using natural language prompts, coordinate points, or bounding boxes. SAM 3 Video can perform open-vocabulary segmentation to recognize and track nearly any object or concept it receives as a prompt, i.e., from simple categories such as “car” to complex, contextual subjects, making it objectively superior to earlier segmentation models that are restricted to fixed label sets. In benchmark tests, it achieves 75 to 80 percent of human-level precision.

SAM 3 Video is optimized for real-world use with the ability to handle videos up to five minutes long, offering fully automated segmentation detailed manual refinement (through prompt-based inputs). This dual capability makes it ideal for tasks requiring accurate, frame-by-frame object tracking while minimizing human intervention, making it perfect for video editing, autonomous systems, AR/VR environments, and advanced analytics applications.

Key Features of SAM 3 Video

• Multi-Modal Prompting: It can sophistically segment objects using natural language text, point coordinates, or bounding boxes.
• Real-Time Video Tracking: It maintains object identity across frames with consistent segmentation masks.
• Open-Vocabulary Detection: It is capable of handling thousands of object categories without retraining.
• Batch Processing Support: It can efficiently processes video sequences with optimized frame inference.
• Flexible Output Formats: It returns either colored overlay videos or binary segmentation masks.
• Interactive Refinement: It seamlessly combines multiple prompt types (text + points + boxes) for precise control.
• Instance and Semantic Segmentation: It tracks individual object instances while understanding broader scene categories.

Best Use Cases

• Video Editing and Post-Production: It is effective in automatically isolate subjects for background replacement, color grading, or visual effects without manual rotoscoping.

• Autonomous Systems: It is perfect for robots and self-driving vehicles as it enables them to track multiple objects simultaneously with semantic understanding.

• Surveillance and Security: It is efficacious in monitoring specific objects or people across video feeds with natural language queries like "red backpack" or "person in blue jacket."

• Sports Analytics: It is useful in tracking players, equipment, and movements across game footage for performance analysis and automated highlight generation.

• Medical Imaging: It can revolutionize healthcare with the ability to segment and track anatomical structures or anomalies in diagnostic video procedures.

• AR/VR Development: It can be utilized to create interactive experiences with real-time object recognition and occlusion handling in mixed reality applications.

Prompt Tips and Output Quality

• Text Prompts: Use specific, descriptive terms for best results; "red sedan" performs better than just "car." You may also test multiple variations to improve the results, therefore, instead of "person wearing hat" use "man with baseball cap"

• Point Prompts: Place points centrally on target objects for precision. Follow the format: [[x, y]] for single points or [[x1, y1], [x2, y2]] for multiple; additionally, use point labels [[1]] for foreground objects you want to track.

• Bounding Box Prompts: Boxes track specific regions across frames exceptionally well, especially in crowded scenes. You can define boxes as [[x_min, y_min, x_max, y_max]].

• Combining Prompts: For complex scenarios, combine text with points or boxes. Example: text prompt "person" + point on a specific individual = tracks that person precisely.

• Video Quality: For best results, use well-lit, stable footage with clear subjects. Low-resolution or heavily compressed videos nrgatively impact the segmentation accuracy.

• Object IDs: To track multiple objects simultaneously, assign unique IDs, such as using integer values like 1 for single objects or [1, 2, 3] for multiple targets.

FAQs

Is SAM 3 Video open-source?
SAM 3 is a foundation model developed by Meta; while Meta has open-sourced previous SAM versions, you must check Meta's official SAM repository for current licensing details and model access.

How is SAM 3 Video different from SAM 2?
SAM Video significantly expands open-vocabulary capabilities, handles far more object categories, and achieves 75-80% of near-human accuracy. Also, it includes specialized variants for video tracking and offers improved real-time performance compared to earlier versions.

What's the maximum video length I can process?
SAM 3 Video supports videos up to 5 minutes in duration; split longer videos into segments or process them in batches.

Should I use text prompts or coordinate-based prompts?
Text prompts are best for clear, distinct objects. On the other hand, use point or box prompts when you need pixel-precise control, have multiple similar objects, or when text descriptions don't capture the exact target.

What output format should I choose?
Enable "Return Overlay Video" for visualization and review as it will show segmented objects with colored masks. You may disable it to receive binary masks to build automation pipelines or for programmatic mask processing.

Can SAM 3 Video track multiple objects simultaneously?
Yes, you may provide multiple prompts (text, points, or boxes) and assign unique Object IDs to each target. Furthermore, SAM 3 Video maintains separate segmentation masks for each tracked object throughout the video sequence.