78 citations · 78 across the 5 of their papers we have counts for
5 papers
GCNext: Towards the Unity of Graph Convolutions for Human Motion Prediction
Xinshun Wang, Qiongjie Cui, Chen Chen +1
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 eac…
Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning
Xinshun Wang, Zhongbin Fang, Xia Li +2
In-context learning provides a new perspective for multi-task modeling for vision and NLP. Under this setting, the model can perceive tasks from prompts and accomplish them without…
Dynamic Dense Graph Convolutional Network for Skeleton-based Human Motion Prediction
Xinshun Wang, Wanying Zhang, Can Wang +2
Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-base…
Learning Snippet-to-Motion Progression for Skeleton-based Human Motion Prediction
Xinshun Wang, Qiongjie Cui, Chen Chen +2
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 exploi…
Graph-Guided MLP-Mixer for Skeleton-Based Human Motion Prediction
Xinshun Wang, Qiongjie Cui, Chen Chen +2
In recent years, Graph Convolutional Networks (GCNs) have been widely used in human motion prediction, but their performance remains unsatisfactory. Recently, MLP-Mixer, initially…