OVFF

Code of paper "Pose Recognition of 3D Human Shapes via Multi-View CNN with Ordered View Feature Fusion"

Paper Download: https://doi.org/10.3390/electronics9091368

Prerequisites

• CUDA and CuDNN (changing the code to run on CPU should require few changes)
• Python 3.6
• Tensorflow-gpu 1.9

Datasets Download and Generate

SH-RE and SH-SY: http://www.cs.cf.ac.uk/shaperetrieval/shrec14/

FAUST: http://faust.is.tue.mpg.de/overview

HPRD: Link: https://pan.baidu.com/s/177328DDAuvUjUV7vPp7tag Password: gpta

RAD: Link: https://pan.baidu.com/s/1uFDYtmxWq8bc3OtgCiqAuA Password: 2lam

Generate dataset views: you can run the script /Others/generateViews_sync.py

Generate rotation dataset (eg: SH-RE-RO): you can run the script /Others/generatePose.py

Generate trainList and testList: you can run the script /Others/generatelist.py

Download network parameters

Alexnet_imagenet Link: https://pan.baidu.com/s/1CJ_RfJF6e269Je0lKTzkjQ password: rfqj

Please copy alexnet_imagenet.npy to ./classification and ./retrieval before run code.

Training and Testing

In each experiment, you can find train.py and test.py used to train and test the network

Citation

If you use our work, please cite our paper

''' Wang, H.; He, P.; Li, N.; Cao, J. Pose Recognition of 3D Human Shapes via Multi-View CNN with Ordered View Feature Fusion. Electronics 2020, 9, 1368. '''

Contact

If you have any problem about this implementation, please feel free to contact via:

hpcalifornia AT 163 DOT com