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-license: cc-by-nc-nd-4.0
+license: cc-by-nc-sa-4.0
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+# RT-Pose: A 4D Radar Tensor-based 3D Human Pose Estimation and Localization Benchmark (ECCV 2024)
+
+RT-Pose introduces a human pose estimation (HPE) dataset and benchmark by integrating a unique combination of calibrated 4D radar tensors, RGB images, and LiDAR point clouds. 
+This integration marks a significant advancement in studying human pose analysis through multi-modality datasets.
+
+
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+#### Sensors
+The data collection hardware system comprises two RGB [cameras](https://www.flir.com/products/blackfly-s-usb3/?model=BFS-U3-16S2C-CS), a non-repetitive
+horizontal scanning [LiDAR](https://www.livoxtech.com/3296f540ecf5458a8829e01cf429798e/assets/horizon/Livox%20Horizon%20user%20manual%20v1.0.pdf), and a cascade imaging [radar module](https://www.ti.com/tool/MMWCAS-RF-EVM).
+![images](./asset/device.pdf)
+
+
+#### Data Statics
+We collect the dataset in 40 scenes with indoor and outdoor environments.
+![images](./asset/examples.pdf)
+
+
+The dataset comprises 72,000 frames distributed across 240 sequences. 
+The structured organization ensures a realistic distribution of human motions, which is crucial for robust analysis and model training.
+
+![images](./asset/data_distribution.pdf)
+
+Please check the paper for more details.
+
+- **Curated by:** Jen-Hao(Andy) Cheng(andyhci@uw.edu), Yuan-Hao Ho (n28081527@gs.ncku.edu.tw) from [Information Processing Lab](https://ipl-uw.github.io/) at University of Washington
+- **License:** [CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en)
+
+### Dataset Sources
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository including data processing and baseline method codes:** [RT-POSE](https://github.com/ipl-uw/RT-POSE)
+- **Paper:** To be viewed on arxiv.
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+1. Download the dataset from Hugging Face (Total data size: ~1.2 TB)
+2. Follow the [data processing tool](https://github.com/ipl-uw/RT-POSE/data_processing) to process radar ADC samples into radar tensors. (Total data size of the downloaded data and saved radar tensors: ~41 TB)
+3. Check the data loading and baseline training, testing codes in the same repo [RT-POSE](https://github.com/ipl-uw/RT-POSE)
+
+## Citation
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+To appear on arxiv
+
+## Dataset Card Contact
+
+Jen-Hao (Andy) Cheng, andyhci@uw.edu