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README.md

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+---
+license: mit
+pipeline_tag: video-classification
+---
+
+# FG-Diff: Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection
+
+This repository contains the pre-trained checkpoints and code for the paper [Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection](https://huggingface.co/papers/2412.03044).
+
+- **Project page:** [https://xiaofeng-tan.github.io/projects/FG-Diff/index.html](https://xiaofeng-tan.github.io/projects/FG-Diff/index.html)
+- **Code:** [https://github.com/xiaofeng-tan/fgdmad-code](https://github.com/xiaofeng-tan/fgdmad-code)
+
+## Overview
+
+FG-Diff is a frequency-guided diffusion model for skeleton-based video anomaly detection. It improves robustness in open-set scenarios through perturbation training and uses frequency information to focus on principal motion components.
+
+## Checkpoints
+
+Pre-trained models are available on Hugging Face: [https://huggingface.co/ModelsWeights/AD-FG-Diff](https://huggingface.co/ModelsWeights/AD-FG-Diff)
+
+## Usage
+
+Please refer to the [GitHub repository](https://github.com/xiaofeng-tan/fgdmad-code) for detailed setup, training, and evaluation instructions.
+
+## Citation
+
+If you find this work useful, please consider citing:
+
+```bibtex
+@article{tan2026fgdiff,
+  title={Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly Detection},
+  author={Tan, Xiaofeng and Wang, Hongsong and Geng, Xin and Wang, Liang},
+  journal={IEEE Transactions on Image Processing},
+  year={2026},
+  doi={10.1109/TIP.2026.3730816}
+}
+```