4 ms·

I'd imagine you could do a frame by frame to identify points of interest and then "predict" the track they should follow into the next frame, training the model

I'd imagine you could do a frame by frame to identify points of interest and then "predict" the track they should follow into the next frame, training the model on each point of interest as you go. In this case, you wouldn't be aiming to identify the material properties specifically, but given a model that's tightly fitted enough, there should be constants that stand in for the missing material properties when it's all said and done. Just an initial thought, I'd love to hear why I'm wrong from those in the field.

qikInNdOutReply 4y ago
That still gives you only indicators on the physical material properties under condition x. Means, you know that with slight wind, a palm tree waves. The net still does not know, how much force it takes to break it and how the material deforms and keeps a different shape beyond certain forces. Why will a lamp post bend permanently in a hurricane, but a palmtree wont? You can derive those properties as a child, by bending a thin metal pipe and watching mums houseplant recover from a application of force. As an adult, you then project that information about thing "classifications" upon the world. Lets take another extreme, a floaties palmtree. How does on derive from the observed visual differences, that this palmtree is stiff and bouncy. Or circumstantial decorations as pysic change indicator. If there is snow on the palmtree, the tree might break in strong winds. To develop this physics reasoning is quite a larger part of childhood and i think the idea, that one can train this into a Neural Net beyond simple "all things are solid and bound to gravity" with references alone is quite a challenging endavour.