Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency
This repository is the PyTorch implementation for the network presented in:
Xingyi Zhou, Arjun Karpur, Chuang Gan, Linjie Luo, Qixing Huang, Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency ECCV 2018(arXiv:1712.05765)
- The following datasets are used in this repo. If you use the data provided, please also consider citing them:
- Download the pre-processing data and annotations here, and un-zip them on
python main.py -expID demo -loadModel ../models/Redwood.pth.tar -test
- Visualize the results.
python tools/vis.py ../exp/Chair/demo/img_valTarget ../exp/Chair/demo/valTarget.txt
- Stage1: Train the source model.
python main.py -expID Source -epochs 120 -dropLR 90
Our results of this stage is provided here.
- Stage2: Adapt to the target domain with shape consistency loss.
python main.py -expID Redwood -targetDataset Redwood -targetRatio 1 -shapeWeight 1 -loadModel ../models/ModelNet120.tar -LR 0.01