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RobustFusion: Human Volumetric Capture with Data-Driven Visual Cues Using a RGBD Camera

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

High-quality and complete 4D reconstruction of human activities is critical for immersive VR/AR experience, but it suffers from inherent self-scanning constraint and consequent fragile tracking under the monocular setting. In this paper, inspired by the huge potential of learning-based human modeling, we propose RobustFusion, a robust human performance capture system combined with various data-driven visual cues using a single RGBD camera. To break the orchestrated self-scanning constraint, we propose a data-driven model completion scheme to generate a complete and fine-detailed initial model using only the front-view input. To enable robust tracking, we embrace both the initial model and the various visual cues into a novel performance capture scheme with hybrid motion optimization and semantic volumetric fusion, which can successfully capture challenging human motions under the monocular setting without pre-scanned detailed template and owns the reinitialization ability to recover from tracking failures and the disappear-reoccur scenarios. Extensive experiments demonstrate the robustness of our approach to achieve high-quality 4D reconstruction for challenging human motions, liberating the cumbersome self-scanning constraint.

Z. Su and L. Xu—Equal Contribution.

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Acknowledgement

This work is supported in part by Natural Science Foundation of China under contract No. 61722209 and 6181001011, in part by Shenzhen Science and Technology Research and Development Funds (JCYJ201805071 83706645).

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Authors and Affiliations

  1. Tsinghua University, Beijing, China

    Zhuo Su, Lan Xu, Zerong Zheng, Tao Yu, Yebin Liu & Lu Fang

  2. ShanghaiTech University, Shanghai, China

    Lan Xu

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  1. Zhuo Su
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  2. Lan Xu
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  3. Zerong Zheng
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  4. Tao Yu
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  5. Yebin Liu
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  6. Lu Fang
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Correspondence to Lu Fang .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Su, Z., Xu, L., Zheng, Z., Yu, T., Liu, Y., Fang, L. (2020). RobustFusion: Human Volumetric Capture with Data-Driven Visual Cues Using a RGBD Camera. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12349. Springer, Cham. https://doi.org/10.1007/978-3-030-58548-8_15

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