Articulation-Aware Canonical Surface Mapping
Nilesh Kulkarni, Abhinav Gupta, David F. Fouhey, Shubham Tulsiani
Requirements
- Python 2.7
- PyTorch tested with
1.2.0and works with1.3.0too. - cuda 9.2
For setup and installation refer to docs/install.md instructions.
Setup Evlaution and Training
For ease of acess we provide python scripts that can generate slurm scripts that can be used to generate the results in the paper.
-
Downloading pre-trained model and annotations. Follow setup instructions here
-
Training from scratch. Follow setup instructions here
Citation
If you find the code useful for your research, please consider citing:-
@inproceedings{kulkarni2020articulation,
title={Articulation-aware Canonical Surface Mapping},
author={Kulkarni, Nilesh and Gupta, Abhinav and Fouhey, David F and Tulsiani, Shubham},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={452--461},
year={2020}
}
Future Release
- Python 3.6
- PyTorch 1.5
- PyTorch3D
as you can see in the following picture, the predict key points are not so close to the ground truth.
I have run this to test:
python -m acsm.benchmark.pascal.kp_project --name=acsm_horse_8parts --category=horse --parts_file=acsm/part_files/horse.txt --use_html --dl_out_pascal=True --dl_out_imnet=False --split=val --num_train_epoch=400 --num_hypo_cams=8 --env_name=acsm_horse_8parts_pck_val --multiple_cam=True --visuals_freq=5 --visualize=True --n_data_workers=4 --scale_bias=0.75 --resnet_style_decoder=True --resnet_blocks=4
Have I done something wrong?
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