Learning Human Motion with Deep Delta Interpolatation

Fig.1 - Comparison of human motion prediction. Left: Our model. Center: Linear interpolation. Right: Ground Truth. Tracer lines represent evolution of joint locations over time.

Fig.2 - Left: The demonstration of the proposed $\Delta$-interpolator approach (green), Linear interpolator baseline (yellow), ground truth motion (white). Positional errors are indicated in red. Right: Robustness of the $\Delta$-interpolator (green) w.r.t. the out-of-distribution operation. Ground truth motion (white), SLERP interpolator (yellow), proposed model with both input and output delta modes disabled (blue). $\Delta$-interpolator is not affected by distribution shift.

If you use this code in any context, please cite the following paper:

@misc{oreshkin2022motion,
      title={Motion Inbetweening via Deep $\Delta$-Interpolator}, 
      author={Boris N. Oreshkin and Antonios Valkanas and Félix G. Harvey and Louis-Simon Ménard and Florent Bocquelet and Mark J. Coates},
      year={2022},
      eprint={2201.06701},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Using this repository

Create workspace and clone this repository

mkdir workspace

cd workspace

git clone https://github.com/boreshkinai/delta-interpolator

Build docker image and launch container

docker build -f Dockerfile -t delta_interpolator:$USER .
nvidia-docker run -p 8888:8888 -p 6006:6006 -v ~/workspace/delta-interpolator:/workspace/delta-interpolator -t -d --shm-size="1g" --name delta_interpolator_$USER delta_interpolator:$USER

Enter docker container and launch training session

docker exec -i -t delta_interpolator_$USER  /bin/bash 

Once inside docker container, this launches the training session for the proposed model. Checkpoints and tensorboard logs are stored in ./logs/lafan/transformer

python run.py --config=src/configs/transformer.yaml

This evaluates zero-velocity and the interpolator models for LaFAN1

python run.py --config=src/configs/interpolator.yaml
python run.py --config=src/configs/zerovel.yaml

To run the Anidance benchmark experiments run:

python run.py --config=src/configs/transformer_infill.yaml

For zero-velocity and interpolator baselines run:

python run.py --config=src/configs/interpolator_anidance.yaml
python run.py --config=src/configs/zerovel_anidance.yaml

Training losses eveolve as follows:

Open the results notebook to look at the metrics:

http://your_server_ip:18888/notebooks/LaFAN1Results.ipynb

The notebook password is default

Pretrained model is available here

https://storage.googleapis.com/delta-interpolator/pretrained_model.zip