Outdoor Markerless Motion Capture with Sparse Handheld Video Cameras

 Yangang Wang, Yebin Liu, Xin Tong, Qionghai Dai, and Ping Tan

1. Deptartment of Automation, Tsinghua University 2. Microsoft Research Asia 3. Simon Fraser University

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Abstract

We present a method for outdoor markerless motion capture with sparse handheld video cameras. In the simplest setting, it only involves two mobile phone cameras following the character. This setup can maximize the ?exibilities of data capture and broaden the applications of motion capture. To solve the character pose under such challenge settings, we exploit the generative motion capture methods and propose a novel model-view consistency that considers both foreground and background in the tracking stage. The background is modeled as a deformable 2D grid, which allows us to compute the background-view consistency for sparse moving cameras. The 3D character pose is tracked with a global-local optimization through minimizing our consistency cost. A novel L1 motion regularizer is also proposed in the optimization to constrain the solution pose space. The whole process of the proposed method is simple as frame by frame video segmentation is not required. Our method outperforms several alternative methods on various examples demonstrated in the paper.

 

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Citation:

Yangang Wang, Yebin Liu, Xin Tong, Qionghai Dai, Ping Tan, Outdoor Markerless Motion Capture with Sparse Handheld Video Cameras, IEEE Transctions on Visualization and Computer Graphics.

@article{outdoormocapTVCG,
  author = {Wang, Yangang and Liu, Yebin and Tong,Xin and Dai, Qionghai and Tan, Ping},
  title = {Robust Non-rigid Motion Tracking and Surface Reconstruction Using L0 Regularization},
  journal = {IEEE Transctions on Visualization and Computer Graphics},
  year = {2017},

}


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