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
UAV-Human
Assembly101
Florence3D
Navigation Turing Test
FR-FS
3DYoga90
Fitness-AQA
MultiviewC
Subtasks
Latest papers with no code
Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks
In this paper we introduce a new skeleton-based representation for 3D action recognition in videos.
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition
Video action recognition, a critical problem in video understanding, has been gaining increasing attention.
Neural Graph Matching Networks for Fewshot 3D Action Recognition
We propose Neural Graph Matching (NGM) Networks, a novel framework that can learn to recognize a previous unseen 3D action class with only a few examples.
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks
For learning and recognition tasks, we design and optimize new D-CNNs based on the idea of Inception Residual networks to predict actions from SPMFs.
3D Human Action Recognition with Siamese-LSTM Based Deep Metric Learning
This paper proposes a new 3D Human Action Recognition system as a two-phase system: (1) Deep Metric Learning Module which learns a similarity metric between two 3D joint sequences using Siamese-LSTM networks; (2) A Multiclass Classification Module that uses the output of the first module to produce the final recognition output.
Coding Kendall's Shape Trajectories for 3D Action Recognition
Grounding on the Riemannian geometry of the shape space, an intrinsic sparse coding and dictionary learning formulation is proposed for static skeletal shapes to overcome the inherent non-linearity of the manifold.
A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition
This paper presents a new framework for human action recognition from a 3D skeleton sequence.
Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks
This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI), for both isolated and continuous action recognition.
Learning clip representations for skeleton-based 3d action recognition
This paper presents a new representation of skeleton sequences for 3D action recognition.
Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions
Then, motion and shape cues are jointly used to generate robust and distinctive spatial-temporal interest points (STIPs): motion-based STIPs and shape-based STIPs.
