Director of Research (if dissertation) or Advisor (if thesis)
Forsyth, David A
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
human pose estimation
occlusion
Language
eng
Abstract
There has been some suspicion that 3D human pose estimation produces significantly worse results on in-the-wild images than on lab images. Confirming this suspicion is difficult, because it is hard to get 3D ground truth for in-the-wild images without measurement equipment significantly affecting the imagery. This thesis (a) demonstrates the suspicions are correct; (b) shows the effect is, at least in part, due to reconstructions not plausible (that is, "like" human poses); (c) explores simple augmentation can improve performance in situations with occlusion and (d) shows that natural methods to produce reconstructions that are plausible produce measurable improvements for in-the-wild reconstruction.
Forcing methods to produce reconstructions that are plausible produces no major improvement on Human3.6M validation data; but this is because error on Human3.6M validation data is a poor predictor of error on in-the-wild data. This thesis shows that a registration error measure applied to reconstructions from multiple view data is a good predictor of ground truth error. Our registration error confirms that various procedures to enforce plausible reconstructions make notable improvements on in-the-wild error consistently across a number of distinct multiple view human action datasets.
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