# Using Marso Studio

{% hint style="info" %}
At this stage, the web renderer we use isn't representative of the quality of the materials. We recommend evalutating in your own renderer.\
\
If you find asset loading times are taking longer than expected, enable hardware acceleration in your browser.
{% endhint %}

#### Marso Studio is a node-based web application. 

The workflow is straightforward:

1. Upload Node — Drag or select your input file. The viewer will display your mesh with its current base texture so you can confirm the asset loaded correctly. 

{% hint style="success" %}
You can also use one of our example assets to run your first generation, these can be found when you click upload model and head to the Example Assets tab.
{% endhint %}

2. Generate PBR Node — Connect to the upload node and run the prediction. The I2M model processes your base texture and produces the PBR texture pack.
3. Review & Download — Inspect the results in the 3D viewer and download the output textures (albedo, roughness, IOR, metallic).

{% hint style="info" %}
You can toggle on Show Normal Map in the 3D viewer within Marso Studio.\
Please note: this does not affect the generated PBR output, only the render within the viewer.
{% endhint %}

***

#### What to Expect — Strengths & Limitations ✅

We want to be upfront about where the model excels and where it currently has rough edges.

#### Where it works well

* Well-defined surfaces — objects with clear material properties (stone, wood, plastic, ceramic) tend to produce strong, convincing PBR.
* Clean photogrammetry — if the scan is good, the PBR prediction will be good. Garbage in, garbage out applies here.

#### Known limitations

* **Metallic prediction can be unstable.** Metallic is the hardest property to predict. The model can sometimes misclassify regions as metallic or non-metallic, particularly on ambiguous surfaces (e.g. painted metal, wet stone, dark plastics). This is an active area of improvement.
* **Coverage artefacts on complex geometry.** We've tuned the number of inference steps to optimise for online performance — keeping prediction times reasonable for a web app. The trade-off is that complex geometry can sometimes show subtle artefacts, especially in high-detail or concave regions where coverage is harder. This is something we can resolve with a higher step count, which produces cleaner, more complete results. The high-step-count mode isn't currently exposed in the web app, but we've included some comparisons below so you can see the difference.

***

#### Feedback

This is a pre-release build and your feedback is incredibly valuable. If you run into issues, see unexpected results, or have suggestions — please let us know. The more specific you can be (which asset, what went wrong, screenshots if possible) the more it helps us improve.


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