Low-Dimensionality Calibration through Local Anisotropic
Scaling for Robust Hand Model Personalization
International Conference on Computer Vision (ICCV), 2017
We present a robust algorithm for personalizing a sphere-mesh tracking model to a user from a collection of depth measurements.
Our core contribution is to demonstrate how simple geometric reasoning can be exploited to build a shape-space, and how its performance is comparable to shape-spaces constructed from datasets of carefully calibrated models.
We achieve this goal by first re-parameterizing the geometry of the tracking template, and introducing a multi-stage calibration optimization.
Our novel parameterization decouples the degrees of freedom for pose and shape, resulting in improved convergence properties.
Our analytically differentiable multi-stage calibration pipeline optimizes for the model in the natural low-dimensional space of local anisotropic scalings, leading to an effective solution that can be easily embedded in other tracking/calibration algorithms.
Compared to existing sphere-mesh calibration algorithms, quantitative experiments assess our algorithm possesses a larger convergence basin, and our personalized models allows to perform motion tracking with superior accuracy.