We formulate a simple human-pose tracking theory from monocular video based on the fundamental relationship between changes in pose and image motion vectors. We investigate the natural embedding of the low-dimensional body pose space into a high-dimensional space of body configurations that behaves locally in a linear manner. The embedded manifold facilitates the decomposition of the image motion vectors into basis motion vector fields of the tangent space to the manifold. This approach benefits from the style invariance of image motion flow vectors, and experiments to validate the fundamental theory show reasonable accuracy (within 4.9 deg of the ground truth).
Tracking human pose from monocular video sequences is a challenging problem due to the large number of independent
parameters affecting image appearance and nonlinear relationships between generating parameters and the resultant
images. Unlike the current practice of fitting interpolation functions to point correspondences between underlying pose
parameters and image appearance, we exploit the relationship between pose parameters and image motion flow vectors
in a physically meaningful way. Change in image appearance due to pose change is realized as navigating a low
dimensional submanifold of the infinite dimensional Lie group of diffeomorphisms of the two dimensional sphere S2.
For small changes in pose, image motion flow vectors lie on the tangent space of the submanifold. Any observed image
motion flow vector field is decomposed into the basis motion vector flow fields on the tangent space and combination
weights are used to update corresponding pose changes in the different dimensions of the pose parameter space. Image
motion flow vectors are largely invariant to style changes in experiments with synthetic and real data where the subjects
exhibit variation in appearance and clothing. The experiments demonstrate the robustness of our method (within ±4° of
ground truth) to style variance.
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