GMU:Critical VR Lab I/F.Z.Ayguler: Difference between revisions

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WHAT IS NEXT
WHAT IS NEXT


-The particle system is one single object. What I need is separate objects for every word. concept of serialization and use it to read from csv file and automatically create Objects.
-The particle system is one single object. In order to get separate objects for every word, first I need to understand the concept of serialization in Unity and use it to read from csv file and automatically create objects.


-When I  have an array of Objects, each object is going to be one word, then, I need to  iterate all objects and use instantiate to create using TextMeshPro-Asset.
-When I  have an array of objects, each object is going to be one word, then, I need to  iterate all objects using TextMeshPro-Asset.


-Finally, I need to create an orbit camera to make the text face the camera from all angles.
-Finally, I need to create an orbit camera to make the text face the camera from all angles.

Revision as of 12:36, 18 June 2020

POINT CLOUD EXPERIMENT


I have been interested in testing the data visualization possibilities while still trying to understand Unity. As an exercise for a larger project, I built a scene with point cloud data and used the walls of the scan as trigger zone collided with a sound. I used Pcx - Point Cloud dataImporter/Renderer for Unity to import binary .ply point cloud file, downloaded the 3D scanned room and the sound.

Screen Shot.png

Video quality is terrible! particles dont look right and movements are choppy. I just couldnt get a smooth video out of OBC.

https://www.youtube.com/watch?v=lURd37SiN6Y&feature=youtu.be




CRITICAL VR PROJECT

Continuing data visualization on Unity, I try to get a multi dimensional graphic extracted from a machine learning algorithm which is a set of language modeling features learning techniques in natural language processing (NLP) technique. It is a two-layer neural networks that are trained to reconstruct linguistic contexts of words. I used an open source algorithm (Word2Vec) which was created, published and patented by a team of researchers by Google in 2013.

I choose a literary work of Jean Baudrillard- Simulacra and Simulation. I used the gensim library’s Word2Vec model to get word-embedding vectors for each word. Word2Vec is used to compute the similarity between words from a large corpus of text. The algorithm is very good at finding most similar words (nearest neighbors), I also tried subtracting and adding words. I am giving an examples to show how the program functions.

Simularca.png

This is the graphic which is reduced the dimensions of the Word2Vec space down to x and y coordinates. Every dot represents a word. Dots that are closer together in a space mean that they are similar.

Indexsimularca3.jpg

In order to get the 3 dimensional data from Word2Vec, I first reduced the dimensions to 3D then, extract the data as a csv table including x y z coordinates and the corresponding word. In principle, I need to know what Unity needs as input and figure out how I can generate the missing information. First solution was to convert .csv file to point cloud and open it in Unity using point cloud data Importer/Renderer. Here is the result:



WHAT IS NEXT

-The particle system is one single object. In order to get separate objects for every word, first I need to understand the concept of serialization in Unity and use it to read from csv file and automatically create objects.

-When I have an array of objects, each object is going to be one word, then, I need to iterate all objects using TextMeshPro-Asset.

-Finally, I need to create an orbit camera to make the text face the camera from all angles.