Learnable triangulation of human pose

Karim Iskakov, Egor Burkov, Victor Lempitsky, Yury Malkov

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    101 Citations (Scopus)


    We present two novel solutions for multi-view 3D human pose estimation based on new learnable triangulation methods that combine 3D information from multiple 2D views. The first (baseline) solution is a basic differentiable algebraic triangulation with an addition of confidence weights estimated from the input images. The second, more complex, solution is based on volumetric aggregation of 2D feature maps from the 2D backbone followed by refinement via 3D convolutions that produce final 3D joint heatmaps. Crucially, both of the approaches are end-to-end differentiable, which allows us to directly optimize the target metric. We demonstrate transferability of the solutions across datasets and considerably improve the multi-view state of the art on the Human3.6M dataset.

    Original languageEnglish
    Title of host publicationProceedings - 2019 International Conference on Computer Vision, ICCV 2019
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Number of pages10
    ISBN (Electronic)9781728148038
    Publication statusPublished - Oct 2019
    Event17th IEEE/CVF International Conference on Computer Vision, ICCV 2019 - Seoul, Korea, Republic of
    Duration: 27 Oct 20192 Nov 2019

    Publication series

    NameProceedings of the IEEE International Conference on Computer Vision
    ISSN (Print)1550-5499


    Conference17th IEEE/CVF International Conference on Computer Vision, ICCV 2019
    Country/TerritoryKorea, Republic of


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