research-article

Human dynamics from monocular video with dynamic camera movements

Authors:

Jehee LeeAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 40, Issue 6

Article No.: 208, Pages 1 - 14

https://doi.org/10.1145/3478513.3480504

Published: 10 December 2021 Publication History

Abstract

We propose a new method that reconstructs 3D human motion from in-the-wild video by making full use of prior knowledge on the laws of physics. Previous studies focus on reconstructing joint angles and positions in the body local coordinate frame. Body translations and rotations in the global reference frame are partially reconstructed only when the video has a static camera view. We are interested in overcoming this static view limitation to deal with dynamic view videos. The camera may pan, tilt, and zoom to track the moving subject. Since we do not assume any limitations on camera movements, body translations and rotations from the video do not correspond to absolute positions in the reference frame. The key technical challenge is inferring body translations and rotations from a sequence of 3D full-body poses, assuming the absence of root motion. This inference is possible because human motion obeys the law of physics. Our reconstruction algorithm produces a control policy that simulates 3D human motion imitating the one in the video. Our algorithm is particularly useful for reconstructing highly dynamic movements, such as sports, dance, gymnastics, and parkour actions.

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Index Terms

Human dynamics from monocular video with dynamic camera movements
1. Computing methodologies
  1. Computer graphics
    1. Animation
      1. Motion processing
      2. Physical simulation
  2. Machine learning
    1. Learning paradigms
      1. Reinforcement learning

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 40, Issue 6

December 2021

1351 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3478513

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Published: 10 December 2021

Published in TOG Volume 40, Issue 6

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Wu YDou ZIshiwaka YOgawa SLou YWang WLiu LKomura T(2024)CBIL: Collective Behavior Imitation Learning for Fish from Real VideosACM Transactions on Graphics10.1145/368790443:6(1-17)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687904
Shen ZPi HXia YCen ZPeng SHu ZBao HHu RZhou X(2024)World-Grounded Human Motion Recovery via Gravity-View CoordinatesSIGGRAPH Asia 2024 Conference Papers10.1145/3680528.3687565(1-11)Online publication date: 3-Dec-2024
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Minarčík JEstep SNi WCrane K(2024)Minkowski Penalties: Robust Differentiable Constraint Enforcement for Vector GraphicsACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657495(1-12)Online publication date: 13-Jul-2024
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Ugrinovic NPan BPavlakos GPaschalidou DShen BSanchez-Riera JMoreno-Noguer FGuibas L(2024)MultiPhys: Multi-Person Physics-Aware 3D Motion Estimation2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00226(2331-2340)Online publication date: 16-Jun-2024
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