a frame of physics pixels is a perspective 3d view based on 2d visual frame from real world

Last updated on October 15, 2025

A frame in physics pixel model/data structure for AI is basically a perspective 3d view with physics parameters labeled or estimated(predicted), which is based on 2d visual or visual like signnal frame from real world.

A pixel on the orignal 2d visual frame which is a physics pixel may overlap several other physics pixels with different depths, all of which share the same XY coordinates but have different Z(depth) coordinates seperately, and of course each physics pixel may have its own physics parameters.

The AI model with physics pixel will learn the perspective relationship (like near large and far small) directly from the frames based on 3d perspetive physics pixels.

Theoretically all human senses or tech signals in real world are somehow perspective from a human or a sensor. So for most real world AI applications except few specific cases, ai model need to be trained by perspective data and working (in reference) with perspective data. For example, you may create a virtual world accurately by calculating physics formulas like Nvidia, but for interacting with signals from real world, AI model needs perspective view both in training and reference, which is exactly what the prime model and the physics pixel provide.

The prime model and the physics pixel dont rely on or need any explicit physics formula and also dont need to create virtual world view from calculation of physcis formulas. On the contrary and also reversely, the prime model and the physics pixel add explicit physics parameters into the data of objects in a perspective frame or the data of the pixels in a perpective frame directly, which make the model learn the correlation between the physics parameters and the visual perspectively directly in training and demand the model estimate/predict the physics parameters from visual perspectively directly in reference.

By the way, the training data for the prime model and the physics pixel can be generated from labeling real world videos with physics parameters by human originally or by calculation of specialized AI or by the model itself iteratively. Of course, a virtual created real world model may be greatly helpful in training on both cost and effectiveness. But finally, AI model for real world application needs to learn from and interact with real world video, which is the ultimate teacher and purpose for any AI.