Towards Accurate 3D Human Motion Prediction from Incomplete Observations

Cui, Qiongjie; Sun, Huaijiang

doi:10.1109/cvpr46437.2021.00477

Cited by 61 publications

(47 citation statements)

References 33 publications

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“…As is shown in Tab 1, representative papers in this category are: SRNN [36], AGED [28], QuaterNet [79], LTD [67], HMR [60], MGCN [110], ARNet [12], LDR [17], LPJP [11], HRI [66], JDM [91], SGRU [62]. MT-GCN [15], MPTC [57], MST-GNN [47], MMA [68].…”

Section: Human Pose Representationmentioning

confidence: 99%

“…Mathematical representation SRNN [36] 2016 AGED [28] 2018 QuaterNet [79] 2018 LTD [67] 2019 HMR [60] 2019 MGCN [110] 2020 HRI [66] 2020 ARNet [12] 2020 LDR [17] 2021 LPJP [11] 2021 TrajectoryCNN [53] 2021 JDM [91] 2021 MMA [68] 2021 MPTC [57] 2021 SGAN [62] 2021 MT-GCN [15] 2021 MPTC [57] 2021 MST-GNN [47] 2021 skeleton as a graph, which makes GCN widely utilized in human pose representation. Additionally, widespread recognition of the Temporal Convolution Networks (TCNs), made the CN-based methods more widely accepted for solving the sequential problem.…”

Section: Physical Representationmentioning

confidence: 99%

“…Additionally, widespread recognition of the Temporal Convolution Networks (TCNs), made the CN-based methods more widely accepted for solving the sequential problem. These methods include: TE [9], PAML [29], CHA [49], LDR [17], MGCN [110], LTD [67], LDR [17], MoPredNet [107], TrajectoryCNN [53], NAT [44], C-seq2seq [45], Q-DCRN [73], MT-GCN [15], LMC [110], MST-GNN [47], DA-GNN [48], JDM [91].…”

Section: Physical Representationmentioning

confidence: 99%

“…CN-based methods TE [9] 2017 C-seq2seq [45] 2018 PAML [29] 2018 CHA [49] 2019 LTD [67] 2019 LDR [17] 2020 MGCN [110] 2020 MoPredNet [107] 2020 TrajectoryCNN [53] 2021 NAT [44] 2021 Q-DCRN [73] 2021 MT-GCN [15] 2021 LMC [110], 2021 MST-GNN [47] 2021 DA-GNN [48] 2021 JDM [91] 2021…”

Section: Prediction Targetmentioning

confidence: 99%

“…The adjacency matrix that represented the weights of the connected joint parts was set as the model parameters. A similar method was also used in [15,91]. Analogously, Mao et al [67] also created a new method to learn an automaticconnected graph.…”

Section: Mathematical Representationmentioning

confidence: 99%

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3D Human Motion Prediction: A Survey

Lyu¹,

Chen²,

Liu³

et al. 2022

Preprint

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3D human motion prediction, predicting future poses from a given sequence, is an issue of great significance and challenge in computer vision and machine intelligence, which can help machines in understanding human behaviors. Due to the increasing development and understanding of Deep Neural Networks (DNNs) and the availability of large-scale human motion datasets, the human motion prediction has been remarkably advanced with a surge of interest among academia and industrial community. In this context, a comprehensive survey on 3D human motion prediction is conducted for the purpose of retrospecting and analyzing relevant works from existing released literature. In addition, a pertinent taxonomy is constructed to categorize these existing approaches for 3D human motion prediction. In this survey, relevant methods are categorized into three categories: human pose representation, network structure design, and prediction target. We systematically review all relevant journal and conference papers in the field of human motion prediction since 2015, which are presented in detail based on proposed categorizations in this survey. Furthermore, the outline for the public benchmark datasets, evaluation criteria, and performance comparisons are respectively presented in this paper. The limitations of the state-of-the-art methods are discussed as well, hoping for paving the way for future explorations.

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