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Skeleton Based Action Recognition

Skeleton-based Action Recognition is a computer vision task that involves recognizing human actions from a sequence of 3D skeletal joint data captured from sensors such as Microsoft Kinect, Intel RealSense, and wearable devices. The goal of skeleton-based action recognition is to develop algorithms that can understand and classify human actions from skeleton data, which can be used in various applications such as human-computer interaction, sports analysis, and surveillance.

( Image credit: View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition )

Papers

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Showing 1–25 of 419 papers

TitleStatusHype
MotionBERT: A Unified Perspective on Learning Human Motion RepresentationsCode3
BlockGCN: Redefine Topology Awareness for Skeleton-Based Action RecognitionCode2
Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action RecognitionCode2
Hulk: A Universal Knowledge Translator for Human-Centric TasksCode2
SkateFormer: Skeletal-Temporal Transformer for Human Action RecognitionCode2
DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action RecognitionCode2
GCN-DevLSTM: Path Development for Skeleton-Based Action RecognitionCode1
Dynamic GCN: Context-enriched Topology Learning for Skeleton-based Action RecognitionCode1
Gimme Signals: Discriminative signal encoding for multimodal activity recognitionCode1
Contrastive Learning from Spatio-Temporal Mixed Skeleton Sequences for Self-Supervised Skeleton-Based Action RecognitionCode1
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action RecognitionCode1
DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology ModelingCode1
Frequency Guidance Matters: Skeletal Action Recognition by Frequency-Aware Mixed TransformerCode1
Fusion-GCN: Multimodal Action Recognition using Graph Convolutional NetworksCode1
Constructing Stronger and Faster Baselines for Skeleton-based Action RecognitionCode1
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical AggregationCode1
BST: Badminton Stroke-type Transformer for Skeleton-based Action Recognition in Racket SportsCode1
Challenges in Video-Based Infant Action Recognition: A Critical Examination of the State of the ArtCode1
CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action RecognitionCode1
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action RecognitionCode1
A Dense-Sparse Complementary Network for Human Action Recognition based on RGB and Skeleton ModalitiesCode1
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action RecognitionCode1
Decoupling GCN with DropGraph Module for Skeleton-Based Action RecognitionCode1
A Spatio-Temporal Multilayer Perceptron for Gesture RecognitionCode1
Anonymization for Skeleton Action RecognitionCode1
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