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Nov 12, 2020
Nov 11, 2020

MEVA: 3D Human Motion Estimation via Motion Compression and Refinement

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3D Human Motion Estimation via Motion Compression and Refinement

Zhengyi Luo, S. Alireza Golestaneh, Kris M. Kitani

ACCV 2020, Oral
[Project website][Quantitative Demo][10min Talk]

Notable

MEVA (Motion Estimation vis Variational Autoencoding) is a video-based 3D human pose estimation method that focus on producing stable and natural-looking human motion from videos. MEVA achieves state-of-the-art human pose estimation accuracy while reducing acceleration error siginificantly. Pleaser refer to our paper for more details.

Updates

  • November 11, 2020 – 14:16 Inference code finished.

Getting Started

Install:

Environment

  • Tested OS: Linux
  • Python >= 3.6

How to install

Install the dependencies:

cat requirements.txt | xargs -n 1 pip install

(The requirements need to installed in certian order)

Running inference/Demo

Prepare necessary data

To run pre-trained models, please run the script:

bash scripts/prepare_data.sh

Command:

python scripts/run_meva_on_video.py --cfg train_meva_2  --vid_file zen_talking_phone.mp4  --output_folder results/output --exp train_meva_2

Training

Training script can be found at scripts/train_meva.py

Prepare Datasets

Coming soon!

Evaluation

Here we compare MEVA with recent state-of-the-art methods on 3D pose estimation datasets. Evaluation metric is Procrustes Aligned Mean Per Joint Position Error (PA-MPJPE) in mm.

Models 3DPW ↓ MPI-INF-3DHP ↓ H36M ↓
SPIN 59.2 67.5 41.1
Temporal HMR 76.7 89.8 56.8
VIBE 56.5 63.4 41.5
MEVA 51.9 62.6 48.1

(The numbers here reflect the current state of this repo, so it might be different from what's in the paper. I did a couple of small improvment to code so it achieved better performance. The changes:

  1. I used an overlapping temporal window when processing the video frames, which largly eliminate the issue introduced at the 3 second transition described in the paper.
  2. I used a new VAE architecture where the VAE does not take in the initial frame and only reconstructs motion from the latent code. )

Eval code can be found at: scripts/eval_meva.py

Known issues

  1. Visulization scale seems off somehow (the humanoid is not scaled properly), still debugging!

Citation

If you find our work useful in your research, please cite our paper MEVA:

@InProceedings{Luo_2020_ACCV,
    author    = {Luo, Zhengyi and Golestaneh, S. Alireza and Kitani, Kris M.},
    title     = {3D Human Motion Estimation via Motion Compression and Refinement},
    booktitle = {Proceedings of the Asian Conference on Computer Vision (ACCV)},
    month     = {November},
    year      = {2020}
}

References:

Notice that this repo builds upon a number of previous great works (especially, VIBE), and borrow scripts from them for convenience. Since MEVA focuses on using a pre-trained VAE on AMASS to breakdown human pose estimation into its coarase-to-fine elements, so the visual training part is heavily borrowed from VIBE. For each file that is borrowed, we indicate that it is referenced and please adhere to their liscnece for usage.

  • Dataloaders, part of the loss function, data pre-processing are from: VIBE
  • SMPL models and layer is from: SMPL-X model
  • Feature extractors are from: SPIN
  • NN modules are from (khrylib): DLOW

About

Official implementation of ACCV 2020 paper "3D Human Motion Estimation via Motion Compression and Refinement" (Identical repo to https://github.com/KlabCMU/MEVA, will be kept in sync)

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