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README.md

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+# SkeletonDiffusion: Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction
+This is the official Huggingface space of the CVPR2025 paper  _Nonisotropic Gaussian Diffusion for Realistic 3D Human Motion Prediction_, codebase available [here](https://github.com/Ceveloper/SkeletonDiffusion/tree/main).
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+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 a nonisotropic Gaussian diffusion formulation reflecting body joint connections. 
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+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).
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+<img src="./media/trailer.gif" alt="trailer" width="512">
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+
+## 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.
 
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