In this thesis, we have analyzed available systems of physically-based materials along with the latest studies in its field. We used the findings of our analysis to develop a Blender add-on with three different approaches for authoring materials. The first approach, Algorithmic, used only available tools in Blender, while the other two approaches, MaterialGAN and NeuralMaterial, used machine learning models. We performed a user study of our implementations, in which we focused on the usability and ease of use of each approach. For this, we used a form, which included the SUS (System Usability Scale) questionnaire. With each of the approaches, users had to perform a task in which they tried to fill a blank 3D scene with materials as similar as possible to those in the target 3D scene. During the study we measured the average task solving time along with the accuracy of user solutions and users' comments about their experience. The results showed that all three approaches have proved extremely useful to the users, but each for different reasons. Both MaterialGAN and NeuralMaterial offered new additions with an iterative workflow and easier extraction of materials directly from flash photos, while Algorithmic was much faster and easier to use, but lacked new worthwile improvements.
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