Abstract
If we consider the appearance of human motion such as gait, facial expression and gesturing, most of such activities result in nonlinear manifolds in the image space. Although the intrinsic body configuration manifolds might be very low in dimensionality, the resulting appearance manifold is challenging to model given various aspects that affects the appearance such as the view point, the person shape and appearance, etc. In this paper we learn decomposable generative models that explicitly decompose the intrinsic body configuration as a function of time from other conceptually orthogonal aspects that affects the appearance such as the view point, the person performing the action, etc. The frameworks is based on learning nonlinear mappings from a conceptual representation of the motion manifold that is homeomorphic to the actual manifold and decompose other sources of variation in the mapping coefficient space.
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Lee, CS., Elgammal, A. (2007). Homeomorphic Manifold Analysis: Learning Decomposable Generative Models for Human Motion Analysis. In: Vidal, R., Heyden, A., Ma, Y. (eds) Dynamical Vision. WDV WDV 2006 2005. Lecture Notes in Computer Science, vol 4358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70932-9_8
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DOI: https://doi.org/10.1007/978-3-540-70932-9_8
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