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Self-supervised Human Mesh Recovery with Cross-Representation Alignment

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

Fully supervised human mesh recovery methods are data-hungry and have poor generalizability due to the limited availability and diversity of 3D-annotated benchmark datasets. Recent progress in self-supervised human mesh recovery has been made using synthetic-data-driven training paradigms where the model is trained from synthetic paired 2D representation (e.g., 2D keypoints and segmentation masks) and 3D mesh. However, on synthetic dense correspondence maps (i.e., IUV) few have been explored since the domain gap between synthetic training data and real testing data is hard to address for 2D dense representation. To alleviate this domain gap on IUV, we propose cross-representation alignment utilizing the complementary information from the robust but sparse representation (2D keypoints). Specifically, the alignment errors between initial mesh estimation and both 2D representations are forwarded into regressor and dynamically corrected in the following mesh regression. This adaptive cross-representation alignment explicitly learns from the deviations and captures complementary information: robustness from sparse representation and richness from dense representation. We conduct extensive experiments on multiple standard benchmark datasets and demonstrate competitive results, hel** take a step towards reducing the annotation effort needed to produce state-of-the-art models in human mesh estimation.

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Authors and Affiliations

  1. University at Buffalo, Buffalo, NY, USA

    Xuan Gong & David Doermann

  2. United Imaging Intelligence, Cambridge, MA, USA

    Xuan Gong, Meng Zheng, Benjamin Planche, Srikrishna Karanam, Terrence Chen & Ziyan Wu

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  1. Xuan Gong
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  2. Meng Zheng
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Correspondence to Xuan Gong .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Gong, X. et al. (2022). Self-supervised Human Mesh Recovery with Cross-Representation Alignment. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13661. Springer, Cham. https://doi.org/10.1007/978-3-031-19769-7_13

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