README.md

---
license: bsd-2-clause
tags:
- human-motion-generation
- human-motion-prediction
- probabilistic-human-motion-generation
pinned: true
language:
- en
---
# SkeletonDiffusion Model Card
This model card focuses on the model associated with the SkeletonDiffusion model, from _Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction_, [arxiv](https://arxiv.org/abs/2501.06035), codebase available [here](https://github.com/Ceveloper/SkeletonDiffusion/tree/main).

SkeletonDiffusion is a probabilistic human motion prediction model that takes as input 0.5s of human motion and generates future motions of 2s with a inference time of 0.4s.
SkeletonDiffusion generates motions that are at the same time realistic and diverse. It is a latent diffusion model that  with a custom graph attention architecture trained with nonisotropic Gaussian diffusion. 

We provide a model for each dataset mentioned in the paper (AMASS, FreeMan, Human3.6M), and a further model trained on AMASS with hands joints (AMASS-MANO).

<img src="https://cdn-uploads.huggingface.co/production/uploads/6501e39f192a9bf2226a864d/sIe8dJwlrWSMSnYiVFCpl.png" alt="drawing" width="600"/>


## Online demo
The model trained on AMASS is accessible in a demo workflow that predicts future motions from videos.
The demo extracts 3D human poses from video via Neural Localizer Fields ([NLF](https://istvansarandi.com/nlf/)) by Sarandi et al., and SkeletonDiffusion generates future motions conditioned on the extracted poses:
SkeletonDiffusion has not been trained with real-world, noisy data, but despite this fact it can handle most cases reasonably.

## Usage

### Direct use
You can use the model for purposes under the BSD 2-Clause License.

### Train and Inference

Please refer to our [GitHub](https://github.com/Ceveloper/SkeletonDiffusion/tree/main) codebase for both usecases.