Hand-Object Contact Prediction (BMVC2021)

This repository contains the code and data for the paper “Hand-Object Contact Prediction via Motion-Based Pseudo-Labeling and Guided Progressive Label Correction” by Takuma Yagi, Md. Tasnimul Hasan and Yoichi Sato.


  • Python 3.6+
  • ffmpeg
  • numpy
  • opencv-python
  • pillow
  • scikit-learn
  • python-Levenshtein
  • pycocotools
  • torch (1.8.1, 1.4.0- for flow generation)
  • torchvision (0.9.1)
  • mllogger
  • flownet2-pytorch

Caution: This repository requires ~100GB space for testing, ~200GB space for trusted label training and ~3TB space for full training.

Getting Started

Download the data

  1. Download EPIC-KITCHENS-100 videos from the official site. Since this dataset uses 480p frames and optical flows for training and testing you need to download the original videos. Place them to data/videos/PXX/PXX_XX.MP4.
  2. Download and extract the ground truth label and pseudo-label (11GB, only required for training) to data/.

Required videos are listed in configs/*_vids.txt.

Clone repository

git clone  --recursive https://github.com/takumayagi/hand_object_contact_prediction.git

Install FlowNet2 submodule

See the official repo to install the custom components.
Note that flownet2-pytorch won’t work on latest pytorch version (confirmed working in 1.4.0).

Download and place the FlowNet2 pretrained model to pretrained/.

Extract RGB frames

The following code will extract 480p rgb frames to data/rgb_frames.
Note that we extract by 60 fps for EK-55 and 50 fps for EK-100 extension.

Validation & test set

for vid in `cat configs/valid_vids.txt`; do bash preprocessing/extract_rgb_frames.bash $vid; done
for vid in `cat configs/test_vids.txt`; do bash preprocessing/extract_rgb_frames.bash $vid; done

Trusted training set

for vid in `cat configs/trusted_train_vids.txt`; do bash preprocessing/extract_rgb_frames.bash $vid; done

Noisy training set

# Caution: take up large space (~400GBs)
for vid in `cat configs/noisy_train_vids.txt`; do bash preprocessing/extract_rgb_frames.bash $vid; done

Extract Flow frames

Similar to above, we extract flow images (in 16-bit png).
This requires the annotation files since we only extract flows used in training/test to save space.

# Same for test, trusted_train, and noisy_train
# For trusted labels (test, valid, trusted_train)
# Don't forget to add --gt
for vid in `cat configs/valid_vids.txt`; do python preprocessing/extract_flow_frames.py $vid --gt; done

# For pseudo-labels
# Extracting flows for noisy_train will take up large space
for vid in `cat configs/noisy_train_vids.txt`; do python preprocessing/extract_flow_frames.py $vid; done

Demo (WIP)

Currently, we only have evaluation code against pre-processed input sequences (& bounding boxes).
We’re planning to release a demo code with track generation.


Download the pretrained models to pretrained/.

Evaluation by test set:

python train.py --model CrUnionLSTMHO --eval --resume pretrained/proposed_model.pth
python train.py --model CrUnionLSTMHORGB --eval --resume pretrained/rgb_model.pth  # RGB baseline
python train.py --model CrUnionLSTMHOFlow --eval --resume pretrained/flow_model.pth  # Flow baseline


python train.py --model CrUnionLSTMHO --eval --resume pretrained/proposed_model.pth --vis

This will produce a mp4 file under <output_dir>/vis_predictions/.


Full training

Download the initial models and place them to pretrained/training/.

python train.py --model CrUnionLSTMHO --dir_name proposed --semisupervised --iter_supervision 5000 --iter_warmup 0 --plc --update_clean --init_delta 0.05  --asymp_labeled_flip --nb_iters 800000 --lr_step_list 40000 --save_model --finetune_noisy_net --delta_th 0.01 --iter_snapshot 20000 --iter_evaluation 20000 --min_clean_label_ratio 0.25

Trusted label training

You can train the “supervised” model by the following:

# Train
python train_v1.py --model UnionLSTMHO --dir_name supervised_trainval --train_vids configs/trusted_train_vids.txt --nb_iters 25000 --save_model --iter_warmup 5000 --supervised

# Trainval
python train_v1.py --model UnionLSTMHO --dir_name supervised_trainval --train_vids configs/trusted_trainval_vids.txt --nb_iters 25000 --save_model --iter_warmup 5000 --eval_vids configs/test_vids.txt --supervised

Optional: Training initial models

To train the proposed model (CrUnionLSTMHO), we first train a noisy/clean network before applying gPLC.

python train.py --model UnionLSTMHO --dir_name noisy_pretrain --train_vids configs/noisy_train_vids_55.txt --nb_iters 40000 --save_model --only_boundary
python train.py --model UnionLSTMHO --dir_name clean_pretrain --train_vids configs/trusted_train_vids.txt --nb_iters 25000 --save_model --iter_warmup 2500 --supervised


  • Set larger –nb_workers an –nb_eval_workers if you have enough number of CPUs.
  • You can set –modality to either rgb or flow if training single-modality models.


Takuma Yagi, Md. Tasnimul Hasan, and Yoichi Sato, Hand-Object Contact Prediction via Motion-Based Pseudo-Labeling and Guided Progressive Label Correction. In Proceedings of the British Machine Vision Conference. 2021.

  title = {Hand-Object Contact Prediction via Motion-Based Pseudo-Labeling and Guided Progressive Label Correction},
  author = {Yagi, Takuma and Hasan, Md. Tasnimul and Sato, Yoichi},
  booktitle = {Proceedings of the British Machine Vision Conference},

When you use the data for training and evaluation, please also cite the original dataset (EPIC-KITCHENS Dataset).


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