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

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12349)

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.

Keywords

Dynamic reconstruction Volumetric capture Robust RGBD camera 

Notes

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).

Supplementary material

Supplementary material 1 (mp4 75221 KB)

504439_1_En_15_MOESM2_ESM.pdf (1.3 mb)
Supplementary material 2 (pdf 1289 KB)

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Tsinghua UniversityBeijingChina
  2. 2.ShanghaiTech UniversityShanghaiChina

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