CAREER: Generalized Separation of Style and Content on Nonlinear Manifolds with Application to Human Motion Analysis

Title: CAREER: Generalized Separation of Style and Content on Nonlinear Manifolds with Application to Human Motion Analysis

The visual input is a function of various conceptually orthogonal factors. Each of these factors, typically, can be represented as an underlying nonlinear manifold. So, in general, each data point lies on a mixture of manifolds. Therefore, we have a product space of all these factors, which makes the problem very challenging. However, the problem can be approached if we understand conceptually, to some extent, the topology, dimensionality and the properties of each individual manifold of the orthogonal factors that generated the data. The ultimate goal of this research is to establish general mathematical frameworks for the separation of multiple factors in the data. In particular, in context of human motion, the objective is to establish a mathematical framework that decouples intrinsic body configuration from other sources of variability that affect the visual input and, consequently, to exploit such models in recovering body configuration. To achieve this goal four research directions will be investigated 1) Learning a unified invariant content manifold representation from various style variations on the same manifold. 2) Learning factorized generative models for the data given representation of one or more of the underlying manifolds. 3) Given representation of the underlying manifold, how that can be used to select discriminative features in the visual input. 4) Applying the findings towards the recovery of intrinsic body configuration.

The problem of separation of style and content is an essential task in visual perception and is a fundamental mystery of perception. It is not clear how we perceive a common motion, such as walking, regardless of all sources of variations in its appearance. The fundamental research problems addressed in this research plan appear extensively in different computer vision as well as machine learning applications. The findings will help promote the state-of-the-art in computer vision and machine learning fields as well as bringing interesting computational models to researchers in the cognitive science field. Human motion analysis will be the main applied domain for this research. The proposed research in human motion analysis has various important applications such as surveillance, security, human computer interaction, etc. Human motion analysis will be the integrating theme between the research and the educational activities for motivating Math and Science education. The educational plan consists of several integrated activities targeting the graduate level, the undergraduate level, and high school educators and students. The goal is to develop educational tools that will integrate the efforts of the PI, high school educators, undergraduate and high school students through collaborating in the design, implementation, and evaluation of a computer vision virtual classroom.

URL: http://www.cs.rutgers.edu/~elgammal/Research/GStyleContent.htm