Have you used or heard about Quest 3? Its virtual representation generation is impeccable. Using 3D sensors and cameras, it showcases physical spaces and multiple shapes and even describes the features of particular objects lying in front of you (if asked). Similarly, the company is planning to introduce another ground-breaking approach: “Meta’s SceneScript for 3D Space Description.”
Meta’s SceneScript for 3D Space Description is a novel methodology that Meta is expected to incorporate with its upcoming AR (augmented reality) glasses. It will help you seamlessly understand the real-time data/information regarding any object placed in your space, from assisting you to navigate the desired location to providing a “digital overlay” of your surroundings. It has the potential to cater to you widely.

Table of contents
- Meta’s SceneScript for 3D Space Description
- Training Techniques Employed for Meta’s SceneScript
- Simulated Real-World Scenarios for Training
- Meta’s SceneScript for 3D Space Description – Incorporating Object Description and Complex Geometry
- Parameter Size of SceneScript Model
- Can We Opt for SceneScript for Outdoor Scenes?
- How Can I Access Meta’s SceneScript for 3D Space Description?
- Wrapping up – “Meta’s SceneScript for 3D Space Description”
Meta’s SceneScript for 3D Space Description
It’s quite challenging to understand the complexities of 3D spaces. Those who belong to the Computer Vision and Machine Learning fields completely understand how complicated it is to predict 3D scene representations from physical objects placed in the real world.
However, Meta’s SceneScript for 3D Space Description is a research project for rescue. For now, you don’t need to use coded rules to get an approximation of a particular location’s architectural components.
Training Techniques Employed for Meta’s SceneScript
You must have heard about “Next Token Prediction”!
If not, then have a look below;
Next token prediction is an advanced technique incorporated in multiple LLMs (large language models) over the past several years. It works like a quick prediction of the upcoming word or phrase in your sentences. Let’s suppose you are typing, “I love having cuisine every Saturday, so this weekend as well, I went to have —.” There is a high expectation that the particular LLM model you are using would predict “cuisine.”

Similarly, the developers have employed similar technology in Meta’s SceneScript. However, the slight twist is that Meta’s SceneScript is for 3D Space Description. So, instead of predicting words or phrases, it will predict the next element in a specific area, such as a turn, wall, window, or door.
Meta has leveraged extensive data for exceptional training of this model. Hence, it perfectly knows how to transform visual or real-world data into a 3D scene. That 3D scene tends to be decoded into a brief description that explains the entire layout of the particular space.
Simulated Real-World Scenarios for Training
While training any sort of LLM, extensive data is used. Usually, developers opt for data that is publicly available on a broad scale. However, if we talk about Meta’s SceneScript for 3D Space Description, it wasn’t easy to find appropriate physical space data to train this model.
However, the Reality Lab Research team decided to design a commendable dataset of indoor spaces. Fast forward a few weeks, they come up with “Aria Synthetic Environments.” As per the resources, it consists of 100K entirely different interiors, indoor locations, and aesthetic environments consisting of multiple objects.
Meta’s SceneScript for 3D Space Description – Incorporating Object Description and Complex Geometry
Furthermore, this model has extreme potential and extensibility that can allow us to enter an exciting era. By utilizing the scene, you will now be capable of predicting the physical environment of any particular location. From architecture to the placement of the objects, it leverages each detail in the final outcome. Plus, it ensures not to miss out on decomposing parts of objects.
Let’s suppose the SceneScript encounters a wardrobe. It will not only represent the wardrobe but also highlight its sections, the placement of clothes, and any object in its surroundings.
Parameter Size of SceneScript Model
Well, no real-world data was available, so it was trained under fully consented environments. However, the approximate parameter size of the SceneScript Model varies around 70M parameters. The model’s training was completed in almost 72 hours, and the process was continuous for 200,000 iterations.
Can We Opt for SceneScript for Outdoor Scenes?
It is definitely possible to use SceneScript for outdoor scenes, but the Research Lab has specifically trained it for synthetic indoor scenes, so outdoor usage would result in slightly capricious outcomes.
How Can I Access Meta’s SceneScript for 3D Space Description?
For now, the officials have only teased a little sneak peek of this synthetic project, which is only accessible to the research team of Meta. Hopefully, you’ll get to experience it in 2024!

Wrapping up – “Meta’s SceneScript for 3D Space Description”
Meta has introduced an impeccable model in 3D scene representation. It not only showcases how AI incorporated with Machine learning can offer such flexibility in reconstruction tasks. However, it also shows endless possibilities for editing, analyzing, or designing the indoor location with a quick 3D analysis.
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