Convert people and objects into 3D with Meta's SAM 3 and SAM 3D

¿How to convert people and objects into 3D models with SAM 3D? Artificial intelligence applied to visuals is making a big impact, and now, in addition to precisely cutting out objects, it's possible convert a single image into a 3D model Ready to explore from multiple angles. Meta has introduced a new generation of tools that bridge editing, visual world understanding, and three-dimensional reconstruction without requiring advanced equipment or knowledge.

We're talking about SAM 3 and SAM 3D, two models that arrive to improve detection, tracking, and segmentation, and to bring the 3D reconstruction of objects and people to a broad audience. Their proposal involves understanding text instructions and visual signals simultaneously, so that cutting, transforming, and reconstructing elements is as easy as typing what we want or making a few clicks.

What are SAM 3 and SAM 3D and how do they differ?

Meta's Segment Anything family expands with two new additions: SAM 3 and SAM 3D. The former focuses on identifying, tracking, and segmenting objects in photos and videos with next-generation accuracy, while the latter Reconstructs 3D geometry and appearance from a single imageincluding people, animals, or everyday products.

The functional difference is clear: SAM 3 handles the "understanding and separating" of visual content, and SAM 3D uses that understanding to "create" a three-dimensional volume. With this pairing, a workflow that previously required complex software or specialized scanners becomes much more accessible and faster.

Furthermore, SAM 3 is not limited to basic visual prompts. It provides natural language-guided segmentation capable of interpreting very precise descriptionsWe no longer talk only about "car" or "ball", but about phrases like "red baseball cap" to locate precisely those elements in a scene, even throughout a video.

Meanwhile, SAM 3D comes in two complementary flavors: SAM 3D Objects, focused on objects and scenesand SAM 3D Body, trained to estimate human shape and body. This specialization allows it to cover everything from consumer goods to portraits and poses, opening the door to creative, commercial, and scientific applications.

How do they manage to segment and reconstruct from a single image?

The key lies in an architecture trained on large volumes of data to establish direct links between words and pixels. The model understands written instructions and visual signals (clicks, dots, or boxes) simultaneously, so that translate a request into specific areas of a photo or a video frame.

This understanding of the language goes beyond traditional class names. SAM 3 can handle complex instructions, exclusions, and nuances, enabling queries like “people sitting down who are not wearing a red hat.” This compatibility with detailed text prompts It solves a historical limitation of previous models, which tended to confuse granular concepts.

Then SAM 3D comes into play: starting with an image, it generates a three-dimensional model that allows you to view the object from other perspectives, reorganize the scene, or apply 3D effects. In practice, it integrates with the previous segmentation to isolate what interests us and, therefore, Rebuild in 3D without complicated intermediate steps.

New features compared to previous generations

SAM 1 and SAM 2 revolutionized segmentation by relying heavily on visual cues. However, they struggled when asked to provide lengthy interpretations or nuanced natural language instructions. SAM 3 breaks through that barrier by incorporating multimodal understanding that connects text and vision more directly.

Meta accompanies the progress with a new benchmark of open vocabulary segmentationDesigned to evaluate text-guided segmentation in real-world scenarios, and with the publication of the SAM 3 weights. In this way, researchers and developers can rigorously measure and compare results between methods.

In its redesign, SAM 3D Objects significantly improves upon previous approaches, according to data shared by Meta, which also releases checkpoints, inference code, and an evaluation set. Alongside SAM 3D Body, the company is releasing SAM 3D Artist Objects, a new dataset created with artists to assess 3D quality in a wide variety of images.

Real-world applications and immediate use cases

Meta is integrating these capabilities into its products. In “Edits,” its video tool for Instagram and Facebook, advanced segmentation is already being used to apply effects to videos. specific people or objects without affecting the rest of the image. This facilitates background changes, selective filters, or targeted transformations without sacrificing quality.

We'll also see these features in Vibes, within the Meta AI app, and on the meta.ai platform, with new editing and creative experiences. By allowing complex instructions, the user can describe what they want to modify, and the system will respond accordingly. automates post-production tasks that used to be laborious.

In commerce, Facebook Marketplace's "View in Room" stands out, helping users visualize how furniture or lamps would look in their home thanks to automatically generated 3D models. This functionality reduces uncertainty and improves the purchase decision, a key point when we cannot physically see the product.

The impact extends to robotics, science, education, and sports medicine. 3D reconstruction from simple photographs can feed simulators, create anatomical reference models, and support analysis tools that previously required specialized equipment. All of this promotes new workflows in research and training.

Segment Anything Playground: test and create without friction

To democratize access, Meta has launched Segment Anything PlaygroundA website where anyone can upload images or videos and experiment with SAM 3 and SAM 3D. Its interface is reminiscent of the "magic wand" of classic editors, with the advantage that we can write what we want to select or refine with a few clicks.

In addition, the Playground offers ready-to-use templates. These include practical options such as pixelate faces or license platesand more creative effects like motion trails or spotlights. This makes it possible to achieve identity protection tasks or eye-catching effects in seconds.

Beyond segmentation, users can explore scenes from new perspectives, rearrange them, or apply three-dimensional effects with SAM 3D. The goal is for anyone, without prior knowledge of 3D or computer vision, to be able to do so. achieve acceptable results in minutes and without installing anything.

Models, open resources and evaluation

Meta has released resources to help the community advance the state of the art. For SAM 3, the following are available: model weights along with an open vocabulary benchmark and a technical paper detailing the architecture and training. This facilitates reproducibility and fair comparisons.

On the 3D front, the company has released control points, inference code, and a next-generation assessment suite. The duality of SAM 3D Objects and SAM 3D Body allows for comprehensive coverage. general objects and the human body with metrics adapted to each case, something essential to assess geometric and visual fidelity.

Collaborating with artists to create SAM 3D Artist Objects introduces aesthetic and diversity criteria into the evaluation, not just technical ones. This is key to making 3D reconstruction useful in creative and commercial environmentswhere the quality perceived by people makes the difference.

Text segmentation: examples and advantages

With SAM 3, you can type "red baseball cap" and the system will identify all matches in an image or throughout a video. This accuracy opens the door to editing workflows where simply typing "red baseball cap" is enough. short and clear sentences to separate elements and apply effects or transformations to them.

Compatibility with multimodal language models allows for richer instructions, including exclusions or conditions (“people sitting down who are not wearing a red cap”). This flexibility reduces manual work hours and decreases selection errors which were previously corrected by hand.

For teams creating content at scale, text-driven segmentation accelerates pipelines and makes it easier to standardize results. In marketing, for example, consistency can be maintained by applying filters to a product family, something that improves time and costs of production.

Social media editing and digital creativity

The integration in Edits brings advanced post-production features to Instagram and Facebook creators. A filter that previously required complex masks can now be applied with a text command and a few clicks, while maintaining the edges and fine details stable frame by frame.

For short pieces, where the publishing schedule matters, this automation is gold. Changing a clip's background, highlighting only one person, or transforming a specific object no longer requires manual workflows, and that democratizes effects that were previously exclusive to professionals.

Meanwhile, Vibes and meta.ai are expanding the range of experiences with language-driven editing and creativity. By being able to describe in detail what we want, the leap from idea to result is shortened, which translates into more creative iterations in less time.

Commerce, science and sport: beyond entertainment

“View in Room” on Facebook Marketplace exemplifies the practical value: seeing a lamp or piece of furniture in your living room before buying reduces returns and builds trust. Behind it is a pipeline that, starting with images, generates a 3D model for visualization contextual.

In science and education, reconstructing from simple photographs reduces the cost of creating teaching materials and realistic simulators. An AI-generated anatomical model can be used as a support tool in classrooms or in... biomechanical analysisaccelerating content preparation.

In sports medicine, combining body composition analysis with form reconstruction provides tools for studying postures and movements without expensive equipment. This opens up possibilities for more frequent evaluations and remote monitoring.

Privacy, ethics and good practices

The power of these tools demands responsibility. Manipulating images of people without their consent can lead to legal and ethical problems. It is advisable to avoid reconstructing images. unfamiliar facesDo not share models without permission and do not alter sensitive scenes that may cause confusion or harm.

Meta announces controls to mitigate misuse, but the ultimate responsibility lies with the user of the technology. It is advisable to verify the origin of images, protect personal data, and assess the context before publishing 3D models that may expose private information.

In professional settings, establishing review and consent policies, and clearly labeling AI-generated content, contributes to responsible use. Training the team on these topics helps to prevent bad practices already respond quickly to incidents.

How to convert people and objects into 3D models with SAM 3D: How to get started

If you want to experiment right away, the Anything Playground Segment is the gateway. There you can upload a photo or video, type what you want to select, and try out 3D reconstruction options within a simple interface. For technical profiles, [further options are available]. weights, checkpoints and code that facilitate customized testing.

Researchers, developers, and artists have an ecosystem that includes benchmarks, evaluation datasets, and documentation. The goal is to establish common ground for measuring progress and accelerating adoption in different sectionsfrom digital creativity to robotics.

The most interesting thing is that this leap isn't reserved for specialists: the learning curve is shortening, and the features are reaching everyday apps. Everything suggests that editing and 3D will continue to be integrated into workflows where natural language is the interface.

With SAM 3 and SAM 3D, Meta brings text segmentation and single-image reconstruction to creators and teams of all sizes. Between the Playground, integration in Edits, open resources, and applications in commerce, education, and sports, a solid foundation is being forged. new way of working with images and volume that combines accuracy, accessibility, and responsibility.