FLEX: Extrinsic Parameter-free Multi-view 3D Human Motion Reconstruction

This repository is the official implementation for the paper

A short clip describing our work: clip.mp4

• Video files showing our results on the Human3.6M dataset: Human36M_S9_Posing_1.mp4 & Human36M_S9_Sitting.mp4
• Video file showing our results on the KTH multi-view Football II dataset: KTH_football.mp4
• Video files comparing MotioNet (single-view) & Iskakov et.al. results versus ours: MotioNet_comparison.mp4 & Iskakov_comparison.mp4
• Syntethic videos from our Blender studio are available to download here

Requirements

• Linux
• Python 3
• NVIDIA GPU + CUDA CuDNN

Run the following command to install other packages:

conda env create -f environment.yml -n <YOUR-ENV-NAME>

Data

• Download the data .zip file from here and unzip it inside the FLEX/data folder.
• Download the pre-trained model checkpoint from here and add it under the FLEX/checkpoint folder.

Evaluation

After you have downloaded the data & pre-trained checkpoint you can evaluate our model by running:

python evaluate_multiview.py --resume=./4_views_mha64_gt.pth --device=<GPU-DEVICE-ID>

Notes:

• In case you are not on a GPU supported machine, just delete the --device flag, and the evaluation will run on CPU.
• In order to save bvh files under FLEX/output folder, add --save_bvh_files argument.

Training

To train the model(s) in the paper, run this command:

Using GT data:

python train.py --batch_size=32 --channel=1024 --n_views=4 --kernel_width=5 --padding=2 --kernel_size_stage_1=5,3,1 --kernel_size_stage_2=5,3,1 --data=gt --n_joints=20 --dilation=1,1,1 --stride=1,1,1 --kernel_size=5,3,1 --transformer_mode=mha --transformer_n_heads=64 --device=<GPU-DEVICE-ID>

Using Iskakov et al. 2D detected pose:

python train.py --batch_size=32 --channel=1024 --n_views=4 --kernel_width=5 --padding=2 --kernel_size_stage_1=5,3,1 --kernel_size_stage_2=5,3,1 --data=learnable --n_joints=20 --dilation=1,1,1 --stride=1,1,1 --kernel_size=5,3,1 --transformer_mode=mha --transformer_n_heads=64 --device=<GPU-DEVICE-ID>

Results

The evaluation script will output some results to the terminal. Here is an example of our pre-trained model output, using ground-truth 2D input:

+--------------+------------+---------------------+
|    Action    | MPJPE (mm) | Acc. Error (mm/s^2) |
+--------------+------------+---------------------+
|  Directions  |   18.04    |         0.54        |
|  Discussion  |   22.03    |         0.73        |
|    Eating    |   20.52    |         0.55        |
|   Greeting   |   20.60    |         1.38        |
|   Phoning    |   22.82    |         0.94        |
|    Photo     |   31.77    |         0.68        |
|    Posing    |   19.68    |         0.70        |
|  Purchases   |   21.88    |         1.02        |
|   Sitting    |   26.98    |         0.49        |
| SittingDown  |   28.65    |         0.81        |
|   Smoking    |   24.05    |         0.93        |
|   Waiting    |   21.06    |         0.58        |
|   WalkDog    |   25.93    |         1.72        |
| WalkTogether |   19.23    |         0.87        |
|   Walking    |   18.92    |         1.09        |
|              |            |                     |
|   Average    |   22.89    |         0.87        |
+--------------+------------+---------------------+

Citation

@inproceedings{gordon2022flex,
  title={FLEX: Extrinsic Parameters-free Multi-view 3D Human Motion Reconstruction},
  author={Gordon, Brian and Raab, Sigal and Azov, Guy and Giryes, Raja and Cohen-Or, Daniel},
  booktitle={European Conference on Computer Vision (ECCV)},
  pages={176--196},
  year={2022},
  organization={Springer}
}