Title: Articulated Pose Estimation in a Learned Smooth Space of Feasible Solutions
Authors: Tai-Peng Tian, Rui Li, Stan Sclaroff
Abstract:
A learning based framework is proposed for estimating human body pose
from a single image. Given a differentiable function that maps from
pose space to image feature space, the goal is to invert the process:
estimate the pose given only image features. The inversion is an
ill-posed problem as the inverse mapping is a one to many
process. Hence multiple solutions exist, and it is desirable to
restrict the solution space to a smaller subset of feasible
solutions. For example, not all human body poses are feasible due to
anthropometric constraints. Since the space of feasible solutions may
not admit a closed form description, the proposed framework seeks to
exploit machine learning techniques to learn an approximation that is
smoothly parameterized over such a space. One such technique is
Gaussian Process Latent Variable Modelling. Scaled conjugate gradient
is then used tond the best matching pose in the space of feasible
solutions when given an input image. The formulation allows easy
incorporation of various constraints, e.g. temporal consistency and
anthropometric constraints. The performance of the proposed approach
is evaluated in the task of upper-body pose estimation from
silhouettes and compared with the Specialized Mapping
Architecture. The estimation accuracy of the Specialized Mapping
Architecture is at least one standard deviation worse than the
proposed approach in the experiments with synthetic data. In
experiments with real video of humans performing gestures, the
proposed approach produces qualitatively better estimation results.