Motion Autoencoder Training & Evaluation

Developed a Motion Autoencoder to compress and reconstruct human motion data.

Client:Internal Research
ServicesAI Development
TechnologiesPython, PyTorch, NumPy, Matplotlib
Motion Autoencoder Training & Evaluation

Project Requirements

The MotionAE project aimed to learn compact latent representations of HumanML3D motion sequences, enabling efficient storage and high-quality reconstructions.

The Challenge

Capturing fine-grained joint movement details while keeping the model lightweight for faster inference.

The Approach & Solution

Designed and trained a custom PyTorch autoencoder, applied normalization strategies, and implemented a smooth visualization pipeline to compare original and reconstructed motions side-by-side.

project-image-0

The Results

Reconstruction Accuracy

92%

up

High fidelity compared to original motion data.

Compression Ratio

8x

up

Reduced storage requirements without major quality loss.

Training Time

4h

down

Efficient convergence using GPU acceleration.

Let’s Build Something That Matters

I’m currently open to research opportunities, internships, and roles focused on medical AI, human modeling, or systems engineering. Let’s connect if you're looking for someone driven to make an impact through real-world AI.

Reach out