Monocular Image 3D Human Pose Estimation under Self-Occlusion

Ibrahim Radwan, Abhinav Dhall, Roland GOECKE

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

51 Citations (Scopus)
1 Downloads (Pure)


In 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 two types of constraints: kinematic constraints and orientation constraints. The kinematic constraints are imposed by projecting a 3D model onto the input image and pruning of the parts, which are incompatible with the anthropomorphism. The orientation constraint 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 HumanEva-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.
Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Computer Vision (ICCV2013)
EditorsKyros Kutulakos, Philip Torr, Steve Seitz, Yi Ma
Place of PublicationPiscataway, USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781479928408
ISBN (Print)9781479930227
Publication statusPublished - 2013
EventIEEE International Conference on Computer Vision (ICCV2013) - Sydney, Sydney, Australia
Duration: 1 Dec 20138 Dec 2013


ConferenceIEEE International Conference on Computer Vision (ICCV2013)


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