Meta's SAM 3 (Free + Open Source)

2026-01-04 · Source: Matthew Berman · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Novice, quick

Summary

Meta has released SAM 3, an open-source and open-weights Segment Anything Model that enables text-prompted segmentation of objects within videos. This model allows users to easily identify and isolate specific items, such as bicycles or taxis, across an entire video sequence, even if they appear mid-frame or are difficult to discern. The system segments objects independently, provides labels and distinct colors for each, and offers a playground interface for interactive searching and object management. This release significantly enhances video analysis capabilities by simplifying object detection and isolation through natural language queries.

Key takeaway

For Computer Vision Engineers developing video analysis tools, SAM 3 offers a robust, open-source solution for object segmentation. You should integrate its text-prompting capabilities to streamline object detection workflows, reducing manual annotation efforts and accelerating the development of applications requiring precise object isolation in dynamic video content.

Key insights

SAM 3 enables text-prompted, open-source video object segmentation for easy analysis.

Principles

• Text prompts simplify object identification.
• Open-source models foster broad utility.

Method

Users input text prompts into a playground interface to search and segment objects across entire video frames, receiving independent labels and colors for each detected item.

In practice

• Segment specific objects like "bicycle" in traffic videos.
• Identify all instances of "taxi" in archival footage.

Topics

• Segment Anything Model
• Video Segmentation
• Text Prompting
• Open-Source AI
• Object Detection

Best for: Computer Vision Engineer, AI Engineer, Machine Learning Engineer, AI Student

Related on AIssential

• SAM 3 - AI at Meta — SAM 3 offers state-of-the-art object segmentation and tracking across images and videos using diverse text and visual prompts.
• SAM 3: Concept-Based Visual Understanding and Segmentation — SAM 3 unifies detection, segmentation, and tracking of open-vocabulary concepts using text or visual prompts.
• SAM 3 for Video: Concept-Aware Segmentation and Object Tracking — SAM3 unifies detection, segmentation, and tracking in video by maintaining streaming memory and tracking state across frames.
• Grounding Qwen3-VL Detection with SAM2 — Combining Qwen3-VL's detection with SAM2's segmentation enables robust, natural language-driven object grounding.
• We’re releasing SAM 3.1: a drop-in update to SAM 3 that introduces object multiplexing to significantly improve video processing efficiency without sacrificing accuracy. We’re sharing this update with the community to help make high-performance applications f - x.com — SAM 3.1 improves video processing efficiency via object multiplexing, enabling high-performance AI on smaller hardware.
• Make 3D models from ANYTHING (Free and Open Source) — Meta's SAM 3D is an open-source, open-weights model that converts 2D images into 3D models, offering significant utility across various applications.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Matthew Berman.