Monocular Image 3D Human Pose Estimation under Self-Occlusion

dc.contributor.authorRadwan, Ibrahim
dc.contributor.authorDhall, Abhinav
dc.contributor.authorGoecke, Roland
dc.coverage.spatialSydney Australia
dc.date.accessioned2015-12-10T23:22:49Z
dc.date.createdDecember 1-8 2013
dc.date.issued2013
dc.date.updated2015-12-10T10:34:47Z
dc.description.abstractIn this paper, an automatic approach for 3D pose reconstruction from a single image is proposed. The presence of human body articulation, hallucinated parts and cluttered background leads to ambiguity during the pose inference, which makes the problem non-trivial. Researchers have explored various methods based on motion and shading in order to reduce the ambiguity and reconstruct the 3D pose. The key idea of our algorithm is to impose both kinematic and orientation constraints. The former is imposed by projecting a 3D model onto the input image and pruning the parts, which are incompatible with the anthropomorphism. The latter is applied by creating synthetic views via regressing the input view to multiple oriented views. After applying the constraints, the 3D model is projected onto the initial and synthetic views, which further reduces the ambiguity. Finally, we borrow the direction of the unambiguous parts from the synthetic views to the initial one, which results in the 3D pose. Quantitative experiments are performed on the Human Eva-I dataset and qualitatively on unconstrained images from the Image Parse dataset. The results show the robustness of the proposed approach to accurately reconstruct the 3D pose form a single image.
dc.identifier.urihttp://hdl.handle.net/1885/66678
dc.publisherIEEE Computer Society
dc.relation.ispartofseries2013 IEEE International conference on Computer Vision (ICCV)
dc.sourceMonocular Image 3D Human Pose Estimation under Self-Occlusion
dc.titleMonocular Image 3D Human Pose Estimation under Self-Occlusion
dc.typeConference paper
local.bibliographicCitation.lastpage1895
local.bibliographicCitation.startpage1888
local.contributor.affiliationRadwan, Ibrahim, University of Canberra
local.contributor.affiliationDhall, Abhinav, College of Engineering and Computer Science, ANU
local.contributor.affiliationGoecke, Roland, College of Engineering and Computer Science, ANU
local.contributor.authoremailu9812468@anu.edu.au
local.contributor.authoruidDhall, Abhinav, u4577817
local.contributor.authoruidGoecke, Roland, u9812468
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080104 - Computer Vision
local.identifier.absfor080602 - Computer-Human Interaction
local.identifier.absseo970108 - Expanding Knowledge in the Information and Computing Sciences
local.identifier.ariespublicationu4334215xPUB1324
local.identifier.doi10.1109/ICCV.2013.237
local.identifier.scopusID2-s2.0-84898826788
local.identifier.uidSubmittedByu4334215
local.type.statusPublished Version

Downloads

Original bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
01_Radwan_Monocular_Image_3D_Human_Pose_2013.pdf
Size:
564.57 KB
Format:
Adobe Portable Document Format
Back to topicon-arrow-up-solid
 
APRU
IARU
 
edX
Group of Eight Member

Acknowledgement of Country

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.


Contact ANUCopyrightDisclaimerPrivacyFreedom of Information

+61 2 6125 5111 The Australian National University, Canberra

TEQSA Provider ID: PRV12002 (Australian University) CRICOS Provider Code: 00120C ABN: 52 234 063 906