Fachbereich Informatik - Aktuell

17.02.2017 09:53

Disputation Matthew Loper

am Donnerstag, 2. März 2017 um 15 Uhr in A301, Sand 1, 2. OG.

Human Shape Estimation Using Statistical Body Models

Berichterstatter 1: Prof. Dr. Hendrik Lensch
Berichterstatter 2: Dr. Michael Black


Human body estimation methods transform real-world observations into predictions about human body state. These estimation methods benefit a variety of health, entertainment, clothing, and ergonomics applications. State may include pose, overall body shape, and appearance.
Body state estimation is underconstrained by observations; ambiguity presents itself both in the form of missing data within observations, and also in the form of unknown correspondences between observations. This challenge can be addressed with the use of a statistical body model: a data-driven virtual human. This helps resolve ambiguity in two ways. First, it fills in missing data, meaning that incomplete observations still result in complete shape estimates. Second, the model provides a statistically-motivated penalty for unlikely states, which enables more plausible body shape estimates.
The contributions of this work include three parts. First, a method for the estimation of body shape, nonrigid deformation, and pose from 3D markers is presented. Second, a concise approach to differentiating through the rendering process, with application to body shape estimation, is presented. And finally, I will present a statistical body model trained from human body scans, with state-of-the-art fidelity, good runtime performance, and compatibility with existing animation packages.