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Found 6 papers, 3 papers with code
GCNext: Towards the Unity of Graph Convolutions for Human Motion Prediction
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.
Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning
Under this setting, the model can perceive tasks from prompts and accomplish them without any extra task-specific head predictions or model fine-tuning.
Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction
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.
Learning Snippet-to-Motion Progression for Skeleton-based Human Motion Prediction
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.
Temporal Decoupling Graph Convolutional Network for Skeleton-based Gesture Recognition
Then, channel-dependent and temporal-dependent adjacency matrices corresponding to different channels and frames are calculated to capture the spatiotemporal dependencies between skeleton joints.
Graph-Guided MLP-Mixer for Skeleton-Based Human Motion Prediction
In recent years, Graph Convolutional Networks (GCNs) have been widely used in human motion prediction, but their performance remains unsatisfactory.
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