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@INPROCEEDINGS{Yue:1035216,
      author       = {Yue, Jiangbei and Li, Baiyi and Petrré, Julien and
                      Seyfried, Armin and Wang, He},
      title        = {{H}uman {M}otion {P}rediction {U}nder {U}nexpected
                      {P}erturbation},
      publisher    = {IEEE},
      reportid     = {FZJ-2025-00301},
      pages        = {1501-1511},
      year         = {2024},
      abstract     = {We investigate a new task in human motion prediction, which
                      is predicting motions under unexpected physical perturbation
                      potentially involving multiple people. Compared with
                      existing research, this task involves predicting less
                      controlled, unpremeditated and pure reactive motions in
                      response to external impact and how such motions can
                      propagate through people. It brings new challenges such as
                      data scarcity and predicting complex interactions. To this
                      end, we propose a new method capitalizing differentiable
                      physics and deep neural networks, leading to an explicit
                      Latent Differentiable Physics (LDP) model. Through
                      experiments, we demonstrate that LDP has high data
                      efficiency, outstanding prediction accuracy, strong
                      generalizability and good explainability. Since there is no
                      similar research, a comprehensive comparison with 11 adapted
                      baselines from several relevant domains is conducted,
                      showing LDP outperforming existing research both
                      quantitatively and qualitatively, improving prediction
                      accuracy by as much as $70\%,$ and demonstrating
                      significantly stronger generalization.},
      month         = {Jun},
      date          = {2024-06-16},
      organization  = {2024 IEEE/CVF Conference on Computer
                       Vision and Pattern Recognition (CVPR),
                       Seattle (WA), 16 Jun 2024 - 22 Jun
                       2024},
      cin          = {IAS-7},
      cid          = {I:(DE-Juel1)IAS-7-20180321},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / CrowdDNA -
                      TECHNOLOGIES FOR COMPUTER-ASSISTED CROWD MANAGEMENT
                      (899739)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)899739},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:001322555901082},
      doi          = {10.1109/CVPR52733.2024.00149},
      url          = {https://juser.fz-juelich.de/record/1035216},
}