SAM 3.1 (Segment Anything Model 3.1)

SAM 3.1 is the latest evolution of Meta’s foundational "Segment Anything" project, released in late March 2026. It represents a major performance and efficiency upgrade over SAM 3, moving beyond simple object selection to a Promptable Concept Segmentation (PCS) framework. While SAM 1 and 2 focused on clicking or boxing specific objects, SAM 3.1 allows users to segment and track every instance of a concept (e.g., "all red cars") across an entire video using natural language or image exemplars.

What It Is

SAM 3.1 is a high-speed, unified foundation model designed for real-time video segmentation and multi-object tracking. The "3.1" update specifically introduces the Multiplexing (MuGS) architecture, which allows the model to process dozens of different objects simultaneously in a single forward pass. It transitions the Segment Anything framework from a single-object interactive tool into an exhaustive "vision-language" engine that can understand and isolate open-vocabulary concepts in complex scenes.

What It Can Do

• Joint Multi-Object Tracking: Simultaneously tracks up to 16 unique objects (or categories) in real-time, maintaining unique IDs for each even during occlusions.
• Promptable Concept Segmentation (PCS): Instead of clicking, you can type "solar panels" or "striped cats," and the model will find and segment every instance appearing in the frame.
• Zero-Shot Generalization: Recognizes and segments objects it has never seen before by leveraging a massive dataset of 4 million unique concepts (SA-Co).
• Image Exemplar Prompting: You can "show" the model a single image of a specific tool or part, and it will find all matching instances in a target video or image library.
• Extreme Memory Efficiency: Uses a shared-memory approach for tracking, reducing VRAM usage by up to 60% compared to running multiple instances of SAM 2.

Examples of Its Capabilities

In professional video editing, SAM 3.1 enables "one-click rotoscoping." A user can type "guitarist and lead singer," and the model will immediately mask both performers throughout the entire concert footage, allowing for instant background replacement or localized color grading.

In industrial inspection, the model can be "shown" a photo of a cracked tile; it can then scan a 4K drone video of a roof, identifying every tile with a similar defect and outputting a precise count and location map. It also excels at 3D scene reconstruction, where its ability to maintain perfect object masks across varying camera angles allows for the creation of high-fidelity 3D models from standard 2D smartphone video.

How Does It Work?

SAM 3.1 utilizes a dual encoder-decoder transformer architecture. It shares a single vision encoder (Perception Encoder) between a DETR-based detector and a SAM 2-inspired tracker.

1. Presence Token: A new architectural component that determines if a requested concept exists in the scene before the model attempts to locate it, drastically reducing "hallucinated" segments.
2. MuGS (Multiplexing): In version 3.1, the model uses a multiplexed decoder that can handle multiple prompt embeddings in parallel.
3. Decoupled Detector-Tracker: By separating "what" an object is (detection) from "where" it moves (tracking), the model avoids the interference that typically slows down multi-object systems.

Applications of SAM 3.1

• Advanced Video Editing: Automating complex rotoscoping, object removal, and "smart" color grading for cinema and social media.
• Autonomous Systems: Providing high-speed environmental awareness for drones and delivery robots, allowing them to categorize and track pedestrians, vehicles, and obstacles simultaneously.
• E-commerce & AR: Automatically generating "shippable" links for every item in a video by identifying and segmenting clothes, furniture, and accessories.
• Scientific Research: Quantifying movements in biological or ecological videos (e.g., tracking every individual bee in a hive or every white blood cell in a micro-fluidic sample).

Previous Models

• SAM 3 (Nov 2025): Introduced the Concept Segmentation (PCS) task and the SA-Co dataset but was significantly slower and more memory-intensive for multi-object tasks.
• SAM 2 (2024): The first version to bring "Segment Anything" to video, introducing memory-based tracking for single objects via points or boxes.
• SAM 1 (2023): The original foundation model that proved high-quality zero-shot image segmentation was possible using a prompt-based transformer.