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20202023
most citedMultiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction

76 citations · 109 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CR2023★ 2 cited

Malicious Agent Detection for Robust Multi-Agent Collaborative Perception

Yangheng Zhao, Zhen Xiang, Sheng Yin +3

Recently, multi-agent collaborative (MAC) perception has been proposed and outperformed the traditional single-agent perception in many applications, such as autonomous driving. Ho…

cs.CV2022★ 1 cited

Number-Adaptive Prototype Learning for 3D Point Cloud Semantic Segmentation

Yangheng Zhao, Jun Wang, Xiaolong Li +4

3D point cloud semantic segmentation is one of the fundamental tasks for 3D scene understanding and has been widely used in the metaverse applications. Many recent 3D semantic segm…

cs.CV2021★ 1 cited

A 3D Mesh-based Lifting-and-Projection Network for Human Pose Transfer

Jinxiang Liu, Yangheng Zhao, Siheng Chen +1

Human pose transfer has typically been modeled as a 2D image-to-image translation problem. This formulation ignores the human body shape prior in 3D space and inevitably causes imp…

cs.CV2021★ 76 cited

Multiscale Spatio-Temporal Graph Neural Networks for 3D Skeleton-Based Motion Prediction

Maosen Li, Siheng Chen, Yangheng Zhao +3

We propose a multiscale spatio-temporal graph neural network (MST-GNN) to predict the future 3D skeleton-based human poses in an action-category-agnostic manner. The core of MST-GN…

cs.CV2020★ 29 cited

Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction

Maosen Li, Siheng Chen, Yangheng Zhao +3

We propose novel dynamic multiscale graph neural networks (DMGNN) to predict 3D skeleton-based human motions. The core idea of DMGNN is to use a multiscale graph to comprehensively…