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Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition in CVPR19
A resource repository for 3D machine learning
The model architecture of AS-GCN (for human action recognition)
C3D is a modified version of BVLC caffe to support 3D ConvNets.
C3D for Keras + TensorFlow
train C3D with keras for action recognition
UESTC RGB-D Varying-view action database. This multi-view action database is captured by Kinect v2.0 with modality of RGB video, 3D skeleton sequences and depth map sequences.
The convertor/conversion of deep learning models for different deep learning frameworks/softwares.
An ensemble neural network (Ensem-NN) for skeleton-based action recognition
Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data
Code and data accompanying the FedGen paper
Source code for "Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching", AAAI2020
A pytorch reproduction of { Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation }.
VPoser: Variational Human Pose Prior
Keras implementation of Graph Convolutional Networks
Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).
MPI-IS Mesh Processing Library
Convolutional Neural Network for 3D meshes in PyTorch
PyTorch implementation of "Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition", CVPR 2020 Oral
fully ready experiments
Info and sample codes for "NTU RGB+D Action Recognition Dataset"
Codes for PKU-MMD dataset.
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