Introduction
On November 19, 2025, Meta AI unveiled SAM 3D, a groundbreaking advancement in computer vision that enables the reconstruction of complete 3D objects and human figures from a single 2D image. Released alongside SAM 3, the newest addition to the Segment Anything Collection, SAM 3D represents a major leap forward in how artificial intelligence understands and reconstructs the three-dimensional world from flat images.
This release marks a significant milestone in Meta's ongoing mission to bridge the gap between 2D imagery and 3D spatial understanding, with immediate real-world applications already deployed across Meta's product ecosystem.
What is SAM 3D?
SAM 3D introduces two specialized architectures designed to handle the complexities of the physical world: SAM 3D Objects, which reconstructs detailed 3D shapes, textures, and layouts from a single image, and SAM 3D Body, which generates accurate 3D human pose and shape estimations even in challenging conditions.
Unlike traditional 3D reconstruction methods that require multiple viewpoints or depth data, SAM 3D can take a flat, two-dimensional image and predict what the object looks like from other angles, effectively turning a standard photo into a rotatable 3D model.
The Two Core Models
SAM 3D Objects
SAM 3D Objects is a foundation model that reconstructs complete 3D geometry, texture, and spatial layout from a single photograph. The model excels in real-world scenarios where objects may be partially occluded or situated in cluttered environments—situations that have traditionally challenged 3D reconstruction systems.
Key capabilities include:
- Reconstruction of everyday objects including furniture, tools, and gadgets
- Complete indoor scene reconstruction with depth, shape, and structure prediction
- Multi-view consistency ensuring models remain coherent from different angles
- Pose-aware 3D mesh generation from masked objects
SAM 3D Body
SAM 3D Body is a promptable model for single-image full-body 3D human mesh recovery that demonstrates state-of-the-art performance with strong generalization and consistent accuracy in diverse in-the-wild conditions.
For human reconstruction, SAM 3D Body leverages a new open-source 3D mesh format called Meta Momentum Human Rig (MHR), which offers enhanced interpretability by separating the skeletal structure and the soft tissue shape. This separation enables more realistic and adjustable human models, opening new possibilities for virtual avatars and animation.
Revolutionary Training Approach
What sets SAM 3D apart from previous 3D reconstruction models is its training methodology. The revolutionary 'Human-in-the-Loop' data engine annotated nearly 1 million physical world images, generating over 3 million verified meshes, bridging the 'sim-to-real' gap and allowing SAM 3D to generalize across diverse real-world scenarios where purely synthetic-trained models fail.
This progressive training approach combined with a data annotation engine that incorporates human feedback allows the system to handle small objects, unusual poses, and difficult situations encountered in uncurated natural scenes.
Technical Architecture
SAM 3D Body employs a Transformer encoder-decoder architecture, enabling prompt-based predictions such as masks and keypoints for high-precision human pose and mesh reconstruction under complex poses and occlusion. The model supports auxiliary prompts, including 2D keypoints and masks, enabling user-guided inference similar to the broader SAM family of models.
The architecture demonstrates remarkable efficiency, with the ability to process and reconstruct 3D models in seconds rather than hours. The system utilizes advanced inference logic that can even hallucinate plausible back-facing geometry for occluded objects, creating complete 3D representations from limited visual information.
Real-World Applications Already Deployed
Facebook Marketplace: View in Room
Meta is using SAM 3D to enable the new View in Room feature on Facebook Marketplace, helping people visualize the style and fit of home decor items, like a lamp or a table, in their spaces before purchasing. This practical application addresses a common friction point in online commerce by allowing buyers to preview furniture in their actual living spaces.
Creative Media Tools
SAM 3 integrates into Meta's Edits video-creation application and the Vibes platform, where it drives effects that permit modifications to designated objects in videos, allowing creators to edit elements selectively without affecting surrounding content.
The Segment Anything Playground
Users can try SAM 3 and SAM 3D on the Segment Anything Playground, Meta's new platform that offers everyone access to cutting-edge models with no technical expertise needed. Users can upload images, use SAM 3D to view scenes from new perspectives, virtually rearrange objects, or add 3D effects like motion trails.
Performance and Capabilities
The performance metrics for SAM 3D are impressive. The model can:
- Reconstruct any object category without limitation, thanks to large-scale pre-training on diverse concepts
- Handle occluded objects and cluttered scenes that challenge traditional methods
- Generate multi-view consistent 3D models suitable for interactive applications
- Export models in standard formats including OBJ, GLB, and the new MHR format for human bodies
Meta developed SAM 3D Artist Objects, described as a first-of-its-kind evaluation dataset specifically designed to challenge existing 3D reconstruction methods and establish new benchmarks for measuring research progress in three-dimensional modeling.
Industry Applications and Future Potential
The implications of SAM 3D extend far beyond consumer applications:
Augmented and Virtual Reality: The ability to quickly generate 3D assets from photos enables more immersive AR/VR experiences and rapid content creation for virtual environments.
Robotics and Spatial Computing: Meta believes SAM 3D has major implications for areas such as robotics, science and sports medicine, as well as creative use cases like supporting the creation of 3D virtual worlds and augmented reality experiences or new assets for video games based on real-world objects and people.
Gaming and Animation: Game developers can now convert real-world objects and people into 3D assets ready for use in Blender, Unity, or Unreal Engine, dramatically accelerating asset creation pipelines.
E-commerce: The "View in Room" feature represents just the beginning of how 3D reconstruction can transform online shopping experiences.
Open Source Commitment
Meta is sharing SAM 3D model checkpoints and inference code, and introducing a novel benchmark for 3D reconstruction with a dataset featuring a diverse array of images and objects, offering a level of realism and challenge that surpasses existing 3D benchmarks.
The company has made the technology accessible through multiple channels:
- Interactive web demo at aidemos.meta.com/segment-anything/editor/convert-image-to-3d
- Complete source code on GitHub at facebookresearch/sam-3d-objects and facebookresearch/sam-3d-body
- Models available through Hugging Face for easy integration
- Detailed Jupyter notebooks demonstrating reconstruction workflows
Technical Requirements and Accessibility
One of SAM 3D's most impressive features is its accessibility. The model is designed to run efficiently on standard hardware, with optimizations that reduce memory usage and enable real-time reconstruction. The Segment Anything Playground allows anyone to experiment with the technology directly in a web browser without requiring specialized equipment or technical knowledge.
Limitations and Future Directions
While SAM 3D represents a major advancement, Meta acknowledges areas for continued improvement. The system works best with clear images and well-defined objects, though its robustness to occlusion and clutter already exceeds previous methods. The company continues to refine the models to handle increasingly complex scenarios and edge cases.
The Broader Context: SAM 3 and SAM 3D Together
SAM 3D doesn't operate in isolation. Meta is simultaneously launching SAM 3, a unified vision model that brings robust, open-vocabulary language understanding to visual segmentation and tracking. Together, these models create a powerful ecosystem where users can identify objects through text prompts with SAM 3, then immediately reconstruct them in 3D with SAM 3D.
This combination enables workflows that were previously impossible or required extensive manual work. For example, a user can type "red chair" to segment all red chairs in an image using SAM 3, then instantly convert each one to a 3D model using SAM 3D.
Conclusion
Meta's SAM 3D represents a fundamental shift in how we approach 3D reconstruction. By moving beyond the limitations of traditional photogrammetry and synthetic-only training, SAM 3D brings semantic understanding and robust performance to real-world 3D reconstruction tasks.
The immediate deployment of this technology across Meta's product ecosystem—from Facebook Marketplace to Instagram's creative tools—demonstrates the maturity and practical utility of the system. More importantly, Meta's commitment to open-sourcing the models, code, and benchmarks ensures that the broader research and development community can build upon this foundation.
As we move toward increasingly spatial computing paradigms with AR glasses, VR headsets, and AI-powered creative tools, the ability to seamlessly convert our 2D world into interactive 3D representations becomes crucial. SAM 3D provides a robust, accessible foundation for this future, democratizing 3D reconstruction and making it available to creators, developers, and researchers worldwide.
The release of SAM 3D is more than just another AI model—it's a glimpse into a future where the boundary between 2D images and 3D reality becomes increasingly fluid, where anyone can capture an object or person with a camera and instantly have a complete, manipulable 3D representation ready for whatever creative or practical purpose they envision.
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