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FLEX: Extrinsic Parameters-free Multi-view 3D Human Motion Reconstruction

  • Conference paper
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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13693))

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Abstract

The increasing availability of video recordings made by multiple cameras has offered new means for mitigating occlusion and depth ambiguities in pose and motion reconstruction methods. Yet, multi-view algorithms strongly depend on camera parameters, particularly on relative transformations between the cameras. Such a dependency becomes a hurdle once shifting to dynamic capture in uncontrolled settings. We introduce FLEX  (Free muLti-view rEconstruXion), an end-to-end extrinsic parameter-free multi-view model. FLEX is extrinsic parameter-free (dubbed ep-free) in the sense that it does not require extrinsic camera parameters. Our key idea is that the 3D angles between skeletal parts, as well as bone lengths, are invariant to the camera position. Hence, learning 3D rotations and bone lengths rather than locations allows for predicting common values for all camera views. Our network takes multiple video streams, learns fused deep features through a novel multi-view fusion layer, and reconstructs a single consistent skeleton with temporally coherent joint rotations. We demonstrate quantitative and qualitative results on three public data sets, and on multi-person synthetic video streams captured by dynamic cameras. We compare our model to state-of-the-art methods that are not ep-free and show that in the absence of camera parameters, we outperform them by a large margin while obtaining comparable results when camera parameters are available. Code, trained models, and other materials are available on https://briang13.github.io/FLEX.

B. Gordon and S. Raab—Equal contribution.

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Acknowledgements

This research would not have been possible without the exceptional support of Mingyi Shi. We are grateful to Kfir Aberman and Yuval Alaluf for reviewing earlier versions of the manuscript, and to Yuval Alaluf and Shahaf Goren for contributing to FLEX’s video clip. This work was supported in part by the Israel Science Foundation (grants no. 2366/16 and 2492/20).

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  1. Tel Aviv University, Tel Aviv, Israel

    Brian Gordon, Sigal Raab, Guy Azov, Raja Giryes & Daniel Cohen-Or

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Correspondence to Brian Gordon .

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  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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Gordon, B., Raab, S., Azov, G., Giryes, R., Cohen-Or, D. (2022). FLEX: Extrinsic Parameters-free Multi-view 3D Human Motion Reconstruction. 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 13693. Springer, Cham. https://doi.org/10.1007/978-3-031-19827-4_11

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