Codebase for Low-Dimensionality Calibration through Local Anisotropic Scaling for Robust Hand Model Personalization (ICCV17)
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.
Put the depth measurements in folder data, similarly to what is done for USER1. To run the calibration and save the calibrated sphere-mesh model to file run respectively main/MAIN_CALIBRATE.m and main/WRITE_CALIBRATED_MODEL_TO_FILE.m Please note that .mex files are built for windows usage, you will have to re-build them to use the code on a different OS.