Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The articulated body models used to represent human motion typically have many degrees of freedom, usually expressed as joint angles that are highly correlated. The true range of motion can therefore be represented by latent variables that span a low-dimensional space. This has often been used to make motion tracking easier. Hover, learning the latent space in a problem- independent way makes it non trivial to initialize the tracking process by picking appropriate initial values for the latent variables, and thus for the pose. ,  show that by directly using observable quantities as our latent variables,  eliminate this problem and achieve full automation given only modest amounts of training data. More specifically,  exploit the fact that the trajectory of a person's feet or hands strongly constrains body pose in motions such as skating, skiing, or golfing. These trajectories are easy to compute and to parameterize using a few variables.  treat these as our latent variables and learn a mapping beten them and sequences of body poses. In this manner, by simply tracking the feet or the hands,  can reliably guess initial poses over whole sequences and, then, refine them.
['Andrea Fossati', 'Mathieu Salzmann', 'Pascal Fua']
I am writing a research paper on a idea Observable subspaces for 3D human motion recovery
[{"from": "human", "value": "I am writing a research paper on a idea Observable subspaces for 3D human motion recovery"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The articulated body models used to represent human motion typically have many degrees of freedom, usually expressed as joint angles that are highly correlated. The true range of motion can therefore be represented by latent variables that span a low-dimensional space. This has often been used to make motion tracking easier. Hover, learning the latent space in a problem- independent way makes it non trivial to initialize the tracking process by picking appropriate initial values for the latent variables, and thus for the pose. ,  show that by directly using observable quantities as our latent variables,  eliminate this problem and achieve full automation given only modest amounts of training data. More specifically,  exploit the fact that the trajectory of a person's feet or hands strongly constrains body pose in motions such as skating, skiing, or golfing. These trajectories are easy to compute and to parameterize using a few variables.  treat these as our latent variables and learn a mapping beten them and sequences of body poses. In this manner, by simply tracking the feet or the hands,  can reliably guess initial poses over whole sequences and, then, refine them."}]