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Procedural Scene Generation from Natural Language

Description

While there are many existing systems which take natural language descriptions and use them to generate images or text, few systems exist to generate 3d renderings or environments based on

While there are many existing systems which take natural language descriptions and use them to generate images or text, few systems exist to generate 3d renderings or environments based on natural language. Most of those systems are very limited in scope and require precise, predefined language to work, or large well tagged datasets for their models. In this project I attempt to apply concepts in NLP and procedural generation to a system which can generate a rough scene estimation of a natural language description in a 3d environment from a free use database of models. The primary objective of this system, rather than a completely accurate representation, is to generate a useful or interesting result. The use of such a system comes in assisting designers who utilize 3d scenes or environments for their work.

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Date Created
  • 2019-05

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Procedural Content Generation Using Noise

Description

Procedural content generation refers to the creation of data algorithmically using controlled randomness. These algorithms can be used to generate complex environments and geological formations as opposed to manually creating

Procedural content generation refers to the creation of data algorithmically using controlled randomness. These algorithms can be used to generate complex environments and geological formations as opposed to manually creating environments, using photogrammetry, or other means. Geological formations and the surrounding terrain can be created using noise based algorithms such as Perlin noise. However, interpreting noise in this manner has a number of challenges due to the pseudo-random nature of noise. We will discuss how to generate noise, how to render noise, and the challenges in interpreting noise.

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Date Created
  • 2021-05