@inproceedings{tan2016fits,
author = {Tan, David Joseph and Cashman, Tom and Taylor, Jonathan and Fitzgibbon, Andrew and Tarlow, Daniel and Khamis, Sameh and Izadi, Shahram and Shotton, Jamie},
title = {Fits Like a Glove: Rapid and Reliable Hand Shape Personalization},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
year = {2016},
month = {June},
abstract = {We present a fast, practical method for personalizing a hand shape basis to an individual user’s detailed hand shape using only a small set of depth images. To achieve this, we minimize an energy based on a sum of render-and-compare cost functions called the golden energy. However, this energy is only piecewise continuous, due to pixels crossing occlusion boundaries, and is therefore not obviously amenable to efficient gradient-based optimization. A key insight is that the energy is the combination of a smooth low-frequency function with a high-frequency, low-amplitude, piecewise continuous function. A central finite difference approximation with a suitable step size can therefore jump over the discontinuities to obtain a good approximation to the energy’s low-frequency behavior, allowing efficient gradient-based optimization. Experimental results quantitatively demonstrate for the first time that detailed personalized models improve the accuracy of hand tracking and achieve competitive results in both tracking and model registration.},
url = {http://approjects.co.za/?big=en-us/research/publication/fits-like-glove-rapid-reliable-hand-shape-personalization/},
}