GMU:Critical VR Lab I/L.-E. Kuehr: Difference between revisions

The resulting vector space is of very high dimensionality, thus cannot be easily visualized. To reduce the high dimensional space to three.dimensions the method ''t-distributed stochastic neighbor embedding'' is used, which keeps words close together that are close in the high dimensional space.

The resulting data is then imported into unity using a ''csv''-file and for every data-point a billboard-text of the word is generated. This process is repeated for every text.

The grass, cuboids and word-spheres are generated and distributed per tile using seeded randomness. Every tile has its own noise and therefore distribution pattern of the objects, making the world even more endless. A swarm of letters using the boid-algorithm guides the player through the world and towards the word-spheres. The closer the view-direction of the viewer is to a word-sphere the more letters are in the swarm and the closer they fly to each other. The boid-algorithm simulates the behaviour of birds by enforcing simple rules for every boid like separation, alignment and cohesion towards all other boids. A compute-shader is used to speed up the simulation process.