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 contribution

    33 Citations (Scopus)
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    Abstract

    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
    Pages1888-1895
    Number of pages8
    ISBN (Electronic)9781479928408
    ISBN (Print)9781479930227
    DOIs
    Publication statusPublished - 2013
    EventIEEE International Conference on Computer Vision (ICCV2013) - Sydney, Sydney, Australia
    Duration: 1 Dec 20138 Dec 2013

    Conference

    ConferenceIEEE International Conference on Computer Vision (ICCV2013)
    CountryAustralia
    CitySydney
    Period1/12/138/12/13

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    Kinematics
    Experiments

    Cite this

    Radwan, I., Dhall, A., & GOECKE, R. (2013). Monocular Image 3D Human Pose Estimation under Self-Occlusion. In K. Kutulakos, P. Torr, S. Seitz, & Y. Ma (Eds.), Proceedings of the IEEE International Conference on Computer Vision (ICCV2013) (pp. 1888-1895). Piscataway, USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICCV.2013.237
    Radwan, Ibrahim ; Dhall, Abhinav ; GOECKE, Roland. / Monocular Image 3D Human Pose Estimation under Self-Occlusion. Proceedings of the IEEE International Conference on Computer Vision (ICCV2013). editor / Kyros Kutulakos ; Philip Torr ; Steve Seitz ; Yi Ma. Piscataway, USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. pp. 1888-1895
    @inproceedings{87f6d38e3ceb4d429cae356afd50b1f8,
    title = "Monocular Image 3D Human Pose Estimation under Self-Occlusion",
    abstract = "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.",
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    Radwan, I, Dhall, A & GOECKE, R 2013, Monocular Image 3D Human Pose Estimation under Self-Occlusion. in K Kutulakos, P Torr, S Seitz & Y Ma (eds), Proceedings of the IEEE International Conference on Computer Vision (ICCV2013). IEEE, Institute of Electrical and Electronics Engineers, Piscataway, USA, pp. 1888-1895, IEEE International Conference on Computer Vision (ICCV2013), Sydney, Australia, 1/12/13. https://doi.org/10.1109/ICCV.2013.237

    Monocular Image 3D Human Pose Estimation under Self-Occlusion. / Radwan, Ibrahim; Dhall, Abhinav; GOECKE, Roland.

    Proceedings of the IEEE International Conference on Computer Vision (ICCV2013). ed. / Kyros Kutulakos; Philip Torr; Steve Seitz; Yi Ma. Piscataway, USA : IEEE, Institute of Electrical and Electronics Engineers, 2013. p. 1888-1895.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    AB - 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.

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    Radwan I, Dhall A, GOECKE R. Monocular Image 3D Human Pose Estimation under Self-Occlusion. In Kutulakos K, Torr P, Seitz S, Ma Y, editors, Proceedings of the IEEE International Conference on Computer Vision (ICCV2013). Piscataway, USA: IEEE, Institute of Electrical and Electronics Engineers. 2013. p. 1888-1895 https://doi.org/10.1109/ICCV.2013.237