1 code implementation • 19 Dec 2023 • Xinshun Wang, Qiongjie Cui, Chen Chen, Mengyuan Liu
The past few years has witnessed the dominance of Graph Convolutional Networks (GCNs) over human motion prediction. Various styles of graph convolutions have been proposed, with each one meticulously designed and incorporated into a carefully-crafted network architecture.
1 code implementation • 6 Dec 2023 • Xinshun Wang, Zhongbin Fang, Xia Li, Xiangtai Li, Chen Chen, Mengyuan Liu
Under this setting, the model can perceive tasks from prompts and accomplish them without any extra task-specific head predictions or model fine-tuning.
no code implementations • 29 Nov 2023 • Xinshun Wang, Wanying Zhang, Can Wang, Yuan Gao, Mengyuan Liu
Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task.
no code implementations • 26 Jul 2023 • Xinshun Wang, Qiongjie Cui, Chen Chen, Shen Zhao, Mengyuan Liu
Existing Graph Convolutional Networks to achieve human motion prediction largely adopt a one-step scheme, which output the prediction straight from history input, failing to exploit human motion patterns.
1 code implementation • IEEE Transactions on Multimedia 2023 • Jinfu Liu, Xinshun Wang, Can Wang, Yuan Gao, Mengyuan Liu
Then, channel-dependent and temporal-dependent adjacency matrices corresponding to different channels and frames are calculated to capture the spatiotemporal dependencies between skeleton joints.
no code implementations • 7 Apr 2023 • Xinshun Wang, Qiongjie Cui, Chen Chen, Shen Zhao, Mengyuan Liu
In recent years, Graph Convolutional Networks (GCNs) have been widely used in human motion prediction, but their performance remains unsatisfactory.
Ranked #3 on Human Pose Forecasting on Human3.6M