MotionAE: Motion Autoencoder on HumanML3D

A minimal autoencoder trained on HumanML3D for motion compression and reconstruction. This page shows the architecture, metrics (MSE/MAE/L2), and example visualizations, with links to code and environment.

arXiv (coming soon) Code Environment.yml Pretrained Weights

Issam Alzouby

UNC Charlotte

Original vs Reconstructed motion teaser — Original vs Reconstructed joint trajectories (teaser). Replace with your own GIF.

Latent space scatter — Latent space preview (e.g., TSNE/UMAP). Replace with your own plot.

Abstract

MotionAE is a lightweight autoencoder for human motion sequences built for clarity and speed. Trained on HumanML3D (SMPL-H, 22 joints, 196 frames, 263-D features), MotionAE learns a compact latent representation that reconstructs trajectories with low error while remaining straightforward to extend to VAE/VQ-VAE variants.

Contributions

Simple, reproducible baseline for motion reconstruction on HumanML3D.
Clean PyTorch code with environment.yml and scripts to train/validate/visualize.
Ready-made figures (joint curves, latent previews) and a one-page project site with light/dark mode.

Architecture

A standard MLP autoencoder. Replace the diagram below with your own (SVG/PNG).

Input

196 × 263 flattened

Latent

512-D

Loss

MSE (reconstruction)

Validation Metrics

From aevalidate.py (averaged over the held-out split).

Metric	Value	Notes
MSE	—	Mean Squared Error (per sample mean)
MAE	—	Mean Absolute Error (per sample mean)
L2	—	Euclidean distance

Tip: export a small metrics.json and fetch it below to auto-fill these numbers.

Results & Visualizations

Drop your PNGs/GIFs into assets/ and update captions.

Joint 0 X over time — Joint 0 — X position vs time (original vs reconstructed).

Joint 5 Y over time — Joint 5 — Y position vs time.

Joint 10 Z over time — Joint 10 — Z position vs time.

Pretrained Weights

Download the pretrained MotionAE checkpoint from Google Drive or via gdown. The page link is also in the hero badges above.

Terminal — Download weights

$ pip install gdown
$ mkdir -p weights && cd weights
$ gdown --fuzzy "https://drive.google.com/file/d/1dYFW_9yYElH_7etZhHoHO2bsn7DB_cQI/view?usp=sharing"
# (optional) verify checksum if provided
$ cd ..
# Example load (adjust path to your script):
$ python -c "import torch; m=torch.load('weights/autoencoder_humanml3d.pth', map_location='cpu'); print(type(m))"

Reproduce

Terminal — Create env

$ conda env create -f environment.yml
$ conda activate momask
$ python MotionAE.py      # trains & saves autoencoder_humanml3d.pth
$ python aevalidate.py    # prints MSE / MAE / L2 and writes assets/metrics.json
$ python visualize.py     # generates plots into assets/

Terminal — GitHub Pages

# Option A: GitHub Pages (static)
# Put index.html and assets/ in your repo root
# Settings → Pages → Source: main /(root)

$ git add .
$ git commit -m "Deploy MotionAE page"
$ git push origin main

Terminal — Vercel / Netlify

# Option B: Vercel (CLI)
$ npm i -g vercel
$ vercel deploy

# Option B: Netlify (drag-and-drop)
# Drop the folder in the Netlify dashboard

BibTeX

@misc{alzouby2025motionae,
  title  = {MotionAE: A Minimal Motion Autoencoder Baseline},
  author = {Issam Alzouby},
  year   = {2025},
  url    = {https://ialzouby.github.io/Motion-AutoEncoder/}
}