The role of Manifold learning in Human Motion Analysis
Prof. Ahmed Elgammal
Abstract
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.