The Role of Manifold Learning in Human Motion Analysis
The human body is an articulated object with high degrees of freedom.
Despite the high dimensionality of the configuration space, many human
motion activities lie intrinsically on low dimensional manifolds.
Although the intrinsic body configuration manifolds might be very low
in dimensionality, the resulting appearance manifolds are challenging
to model given various aspects that affects the appearance such as the
shape and appearance of the person performing the motion, or variation
in the view point, or illumination. Our objective is to learn
representations for the shape and the appearance of moving (dynamic)
objects that support tasks such as synthesis, pose recovery,
reconstruction, and tracking. We studied various approaches for
representing global deformation manifolds that preserve their
geometric structure. Given such representations, we can learn
generative models for dynamic shape and appearance. We also address
the fundamental question of separating style and content on nonlinear
manifolds representing dynamic objects.We learn factorized generative
models that explicitly decompose the intrinsic body configuration
(content) as a function of time from the appearance/shape (style
factors) of the person performing the action as time-invariant
parameters. We show results on pose recovery, body tracking, gait
recognition, as well as facial expression tracking and recognition.