Robust and automatic motion-capture data recovery using soft skeleton constraints and model averaging.

ABSTRACT: Motion capture allows accurate recording of human motion, with applications in many fields, including entertainment, medicine, sports science and human computer interaction. A common difficulty with this technology is the occurrence of missing data, due to occlusions, or recording conditions. Various models have been proposed to estimate missing data. Some are based on interpolation, low-rank properties or inter-correlations. Others involve dataset matching or skeleton constraints. While the latter have the advantage of promoting a realistic motion estimation, they require prior knowledge of skeleton constraints, or the availability of a prerecorded dataset. In this article, we propose a probabilistic averaging method of several recovery models (referred to as Probabilistic Model Averaging

SUBMITTER: Tits M 

PROVIDER: S-EPMC6039011 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature