--- 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). drawing ## 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.