# How HyperGen™ Works

Last updated

Last updated

When the user inputs a text prompt or uploads an image, the platform initiates a complex analysis process where each pixel and feature within the image is meticulously evaluated and converted into geometric data points. Through intricate pattern recognition and feature extraction techniques, our system accurately interprets the spatial relationships, textures, color gradients, and shapes present in the 2D image, subsequently generating a highly detailed and realistic 3D model that faithfully captures the essence and intricacies of the original image or the text prompt requested.

**Let: **

$I_u$: User uploaded image or text prompt.

$P$: Process of text prompt, pixel or feature evaluation.

$G$: Geometric data points.

$R$: Pattern recognition and feature extraction techniques.

$S$: Spatial relationships, textures, color gradients, and shapes.

$M$: Generated 3D model.

$N$: Neural networks and deep learning models.

$T$: Training datasets of 2D images and corresponding 3D models.

$F$: Final optimized 3D model.

**The process can be described as follows:**

$I_u \rightarrow P(I_u) \rightarrow G \rightarrow R(G) \rightarrow S \rightarrow M \rightarrow N(M, T) \rightarrow F$

**Where:**

$I_u$ User uploaded image or text prompt.

$P(I_u)$: Process that evaluates the text prompt or the pixels and features in the image.

$G$: Geometric data points derived.

$R(G)$: Techniques that extract patterns and features from the geometric data.

$S$: Spatial relationships, textures, color gradients, and shapes extracted from the image.

$M=GenerateModel(S)$: Generation of a 3D model based on the extracted features.

$F=N(M,T)$: Final optimized 3D model after refinement by neural networks and training datasets.