David Joseph Tan (1,2), Thomas Cashman (1), Jonathan Taylor (3), Andrew Fitzgibbon (1), Daniel Tarlow (1), Sameh Khamis (3), Shahram Izadi (3), Jamie Shotton (1)
(1) Microsoft Research; (2) TU Munich; (3) perceptiveIO
Note: This work was conducted at Microsoft Research
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.