Improving 3D human pose estimation in-the-wild

Gonzalez, Victor

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https://hdl.handle.net/2142/115430 Copy

Description

Title

Improving 3D human pose estimation in-the-wild

Author(s)

Gonzalez, Victor

Issue Date

2022-04-25

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

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.

Graduation Semester

2022-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2022 Victor Gonzalez

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