motion-prediction-tim

This repo contains the code for the ACCV 2020 paper Motion Prediction using Temporal Inception Module. We only support the H3.6M dataset. Implementation is done in PyTorch.

Download data

The human3.6m dataset is in exponential map format.

git clone https://github.com/tileb1/motion-prediction-tim
cd motion-prediction-tim
mkdir data
cd data
wget http://www.cs.stanford.edu/people/ashesh/h3.6m.zip
unzip h3.6m.zip
rm h3.6m.zip
cd ..

Dependencies

• Download PyTorch with cuda (tested with 1.6.0)
• pip install -r requirements.txt

Train the model

Run the main_3d.py file. Command line args are defined in opt.py. The default configuration is for short-term prediction.

python main_3d.py

Results

Average MPJE for short term prediction over 5 runs (all actions included)

80ms	160ms	320ms	400ms
11.4	24.3	50.4	60.9

Average MPJE for long term prediction over 5 runs (walking, eating, discussion, smoking)

560ms	1000ms
49.6	68.6

Long term visualisation of our method compared to previous SOTA Mao et al. (2019)

Pretrained models

Two pretrained models (long term and short term) are to be found in checkpoint/pretrained and can be loaded with

from utils.opt import Options
import utils.model as nnmodel
import torch

opt = Options().parse()
model = nnmodel.InceptionGCN(opt.linear_size, opt.dropout, num_stage=opt.num_stage, node_n=66, opt=opt)
model.load_state_dict(torch.load('checkpoint/pretrained/name.pth.tar', map_location=torch.device('cpu'))['state_dict'])

You can also run a demo of the prediction vs groundtruth by calling

python demo.py

Citing

If you use our code, please cite our work

@InProceedings{Lebailly_2020_ACCV,
    author    = {Lebailly, Tim and Kiciroglu, Sena and Salzmann, Mathieu and Fua, Pascal and Wang, Wei},
    title     = {Motion Prediction Using Temporal Inception Module},
    booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)},
    month     = {November},
    year      = {2020}
}

Acknowledgments

This code builds on top of LearnTrajDep by Mao et al. (2019) which is itself adapted from 3d-pose-baseline. The visualization code is adapted from Residual Sup. RNN.