Automating
Reality.
One of the biggest bottlenecks in 3D development is asset creation. We developed a machine learning pipeline to instantly infer volumetric geometry from standard 2D photographs.
100x
Faster Workflow
99%
Depth Accuracy
5s
Generation Time
.OBJ
Universal Output
Manual Modeling is Slow.
Populating a virtual world with thousands of unique objects typically requires an army of 3D artists. The challenge was to democratize this process, allowing anyone to turn a photo they took on their phone into a game-ready asset.
Machine Learning Pipeline
From pixels to polygons in three steps.
1. Depth Inference
The system analyzes luminance and perspective cues in the 2D image to generate a high-bitrate depth map, predicting the Z-axis for every pixel.
2. Mesh Generation
This point cloud is triangulated into a continuous mesh. We apply automated retopology algorithms to ensure clean edge loops suitable for animation.
3. Texture Projection
The original image is projected back onto the new 3D surface as a UV-mapped texture, completing the illusion.
Input vs. Output
Compare the original 2D input photograph with the generated 3D geometry.