3D Action Recognition
34 papers with code • 3 benchmarks • 14 datasets
Image: Rahmani et al
Benchmarks
These leaderboards are used to track progress in 3D Action Recognition
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Libraries
Use these libraries to find 3D Action Recognition models and implementationsDatasets
NTU RGB+D
BABEL
Assembly101
UAV-Human
Florence3D
Navigation Turing Test
FR-FS
3DYoga90
Fitness-AQA
MultiviewC
Subtasks
Most implemented papers
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
adityac94/Grad_CAM_plus_plus
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Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems.
TSM: Temporal Shift Module for Efficient Video Understanding
MIT-HAN-LAB/temporal-shift-module
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ICCV 2019
The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost.
Unsupervised Learning of Object Keypoints for Perception and Control
deepmind/deepmind-research
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NeurIPS 2019
In this work we aim to learn object representations that are useful for control and reinforcement learning (RL).
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
benedekrozemberczki/pytorch_geometric_temporal
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CVPR 2019
In addition, the second-order information (the lengths and directions of bones) of the skeleton data, which is naturally more informative and discriminative for action recognition, is rarely investigated in existing methods.
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
kenziyuliu/ms-g3d
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CVPR 2020
Spatial-temporal graphs have been widely used by skeleton-based action recognition algorithms to model human action dynamics.
Revisiting Skeleton-based Action Recognition
open-mmlab/mmaction2
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CVPR 2022
In this work, we propose PoseC3D, a new approach to skeleton-based action recognition, which relies on a 3D heatmap stack instead of a graph sequence as the base representation of human skeletons.
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
Recent approaches in depth-based human activity analysis achieved outstanding performance and proved the effectiveness of 3D representation for classification of action classes.
Reconstructing undersampled photoacoustic microscopy images using deep learning
axd465/PAM_Deep_Learning_Undersampling_Publication
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One primary technical challenge in photoacoustic microscopy (PAM) is the necessary compromise between spatial resolution and imaging speed.
Interpretable 3D Human Action Analysis with Temporal Convolutional Networks
In this work, we propose to use a new class of models known as Temporal Convolutional Neural Networks (TCN) for 3D human action recognition.
Investigation of Different Skeleton Features for CNN-based 3D Action Recognition
The proposed method achieved state-of-the-art performance on NTU RGB+D dataset for 3D human action analysis.
