|
|
--- |
|
|
license: bsd-2-clause |
|
|
tags: |
|
|
- human-motion-generation |
|
|
- human-motion-prediction |
|
|
- probabilistic-human-motion-generation |
|
|
pinned: true |
|
|
language: |
|
|
- en |
|
|
datasets: |
|
|
- wjwow/FreeMan |
|
|
--- |
|
|
# 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_, 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="./media/trailer.gif" alt="trailer" width="512"> |
|
|
|
|
|
|
|
|
## 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. |
|
|
|
