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DTSTAMP:20240214T070250Z
LOCATION:Meeting Room C4.11\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231215T155000
DTEND;TZID=Australia/Melbourne:20231215T160000
UID:siggraphasia_SIGGRAPH Asia 2023_sess139_papers_222@linklings.com
SUMMARY:Fusing Monocular Images and Sparse IMU Signals for Real-time Human
  Motion Capture
DESCRIPTION:Technical Papers\n\nShaohua Pan, Qi Ma, and Xinyu Yi (Tsinghua
  University); Weifeng Hu, Xiong Wang, Xingkang ZHOU, and Jijunnan LI (OPPO
  Research Institute); and Feng Xu (Tsinghua University)\n\nEither RGB imag
 es or inertial signals have been used for the task of motion capture (moca
 p), but combining them together is a new and interesting topic. We believe
  that the combination is complementary and able to solve the inherent diff
 iculties of using one modality input, including occlusions, extreme lighti
 ng/texture, and out-of-view for visual mocap and global drifts for inertia
 l mocap. To this end, we propose a method that fuses monocular images and 
 sparse IMUs for real-time human motion capture. Our method contains a dual
  coordinate strategy to fully explore the IMU signals with different goals
  in motion capture. To be specific, besides one branch transforming the IM
 U signals to the camera coordinate system to combine with the image inform
 ation, there is another branch to learn from the IMU signals in the body r
 oot coordinate system to better estimate body poses. Furthermore, a hidden
  state feedback mechanism is proposed for both two branches to compensate 
 for their own drawbacks in extreme input cases. Thus our method can easily
  switch between the two kinds of signals or combine them in different case
 s to achieve a robust mocap. Quantitative and qualitative results demonstr
 ate that by delicately designing the fusion method, our technique signific
 antly outperforms the state-of-the-art vision, IMU, and combined methods o
 n both global orientation and local pose estimation.\n\nRegistration Categ
 ory: Full Access\n\nSession Chair: Yuting Ye (Reality Labs Research, Meta)
URL:https://asia.siggraph.org/2023/full-program?id=papers_222&sess=sess139
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