Improve dataset card: Add description, links, task category, sample usage, and citation

README.md

@@ -1,3 +1,146 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- robotics
+tags:
+- humanoid-locomotion
+- motion-imitation
+- physically-grounded
+---
+
+# PHUMA: Physically-Grounded Humanoid Locomotion Dataset
+
+[![arXiv](https://img.shields.io/badge/arXiv-2510.26236-b31b1b.svg)](https://arxiv.org/abs/2510.26236)
+[![Project Page](https://img.shields.io/badge/Project_Page-Visit-blue.svg)](https://davian-robotics.github.io/PHUMA/)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-yellow)](https://huggingface.co/datasets/DAVIAN-Robotics/PHUMA)
+
+Motion imitation is a promising approach for humanoid locomotion, enabling agents to acquire humanlike behaviors. Existing methods typically rely on high-quality motion capture datasets such as AMASS, but these are scarce and expensive, limiting scalability and diversity. Recent studies attempt to scale data collection by converting large-scale internet videos, exemplified by Humanoid-X. However, they often introduce physical artifacts such as floating, penetration, and foot skating, which hinder stable imitation.
+
+In response, we introduce PHUMA, a Physically-grounded HUMAnoid locomotion dataset that leverages human video at scale, while addressing physical artifacts through careful data curation and physics-constrained retargeting. PHUMA enforces joint limits, ensures ground contact, and eliminates foot skating, producing motions that are both large-scale and physically reliable.
+
+**Paper:** [PHUMA: Physically-Grounded Humanoid Locomotion Dataset](https://huggingface.co/papers/2510.26236)
+**Project Page:** https://davian-robotics.github.io/PHUMA
+**Code:** https://github.com/davian-robotics/PHUMA
+
+## Sample Usage
+
+This section provides a quick guide to installing the necessary environment and running examples from the PHUMA data pipeline. For more detailed instructions, please refer to the [GitHub repository](https://github.com/davian-robotics/PHUMA).
+
+### Prerequisites
+- Python 3.9
+- CUDA 12.4 (recommended)
+- Conda package manager
+
+### Installation
+
+1.  **Clone the repository:**
+    ```bash
+    git clone https://github.com/DAVIAN-Robotics/PHUMA.git
+    cd PHUMA
+    ```
+
+2.  **Set up the environment:**
+    ```bash
+    conda create -n phuma python=3.9 -y
+    conda activate phuma
+    ```
+
+3.  **Install dependencies:**
+    ```bash
+    pip install -r requirements.txt
+    pip install -e .
+    ```
+
+## Dataset Pipeline
+
+### 1. Physics-Aware Motion Curation
+
+Our physics-aware curation pipeline filters out problematic motions from human motion data to ensure physical plausibility.
+
+**Starting Point:** We begin with the Humanoid-X collection as described in our paper. For more details, refer to the [Humanoid-X repository](https://github.com/sihengz02/UH-1).
+
+**Required SMPL-X Models:** Before running the curation pipeline, you need to download the SMPL-X model files:
+
+1.  Visit [SMPL-X official website](https://smpl-x.is.tue.mpg.de/)
+2.  Register and download the following files:
+    - `SMPLX_FEMALE.npz` and `SMPLX_FEMALE.pkl`
+    - `SMPLX_MALE.npz` and `SMPLX_MALE.pkl`
+    - `SMPLX_NEUTRAL.npz` and `SMPLX_NEUTRAL.pkl`
+3.  Place all downloaded files in the `asset/human_model/smplx/` directory
+
+**Example Usage:**
+```bash
+# Set your project directory
+PROJECT_DIR="[REPLACE_WITH_YOUR_WORKING_DIRECTORY]/PHUMA"
+cd $PROJECT_DIR
+
+# We provide an example clip: data/human_pose/example/kick.npy
+human_pose_file="example/kick"
+
+python src/curation/preprocess_smplx.py \
+    --project_dir $PROJECT_DIR \
+    --human_pose_file $human_pose_file \
+    --visualize 0
+```
+
+**Output:**
+- Preprocessed motion chunks: `example/kick_chunk_0000.npy` and `example/kick_chunk_0001.npy` under `data/human_pose_preprocessed/`
+- If you set `--visualize 1`, will also save `example/kick_chunk_0000.mp4` and `example/kick_chunk_0001.mp4` under `data/video/human_pose_preprocessed/`
+
+### 2. Physics-Constrained Motion Retargeting
+
+To address artifacts introduced during the retargeting process, we employ **PhySINK**, our physics-constrained retargeting method that adapts curated human motion to humanoid robots while enforcing physical plausibility.
+
+**Shape Adaptation (One-time Setup):**
+```bash
+# Find the SMPL-X shape that best fits a given humanoid robot
+# This process only needs to be done once and can be reused for all motion files
+python src/retarget/shape_adaptation.py \
+    --project_dir $PROJECT_DIR \
+    --robot_name g1
+```
+
+**Output:** Shape parameters saved to `asset/humanoid_model/g1/betas.npy`
+
+**Motion Adaptation:**
+```bash
+# Using the curated data from the previous step for Unitree G1 humanoid robot
+
+human_pose_preprocessed_file="example/kick_chunk_0000"
+
+python src/retarget/motion_adaptation.py \
+    --project_dir $PROJECT_DIR \
+    --robot_name g1 \
+    --human_pose_file $human_pose_preprocessed_file \
+    --visualize 0
+```
+
+**Output:**
+- Retargeted humanoid motion data: `data/humanoid_pose/g1/example/kick_chunk_0000.npy`
+- If you set `--visualize 1`, will also save `data/video/humanoid_pose/example/kick_chunk_0000.mp4`
+
+## Motion Tracking and Evaluation
+
+To reproduce our reported quantitative results, use the provided data splits located in `data/split/`:
+- `phuma_train.txt`
+- `phuma_test.txt`
+- `unseen_video.txt`
+
+LAFAN1 Retargeted Data: Available [here](https://huggingface.co/datasets/lvhaidong/LAFAN1_Retargeting_Dataset).
+
+LocoMuJoCo Retargeted Data: Available [here](https://github.com/robfiras/loco-mujoco).
+
+For motion tracking and path following tasks, we utilize the codebase from [MaskedMimic](https://github.com/NVlabs/ProtoMotions).
+
+## Citation
+
+If you use this dataset or code in your research, please cite our paper:
+
+```bibtex
+@article{lee2025phuma,
+  title={PHUMA: Physically-Grounded Humanoid Locomotion Dataset},
+  author={Kyungmin Lee and Sibeen Kim and Minho Park and Hyunseung Kim and Dongyoon Hwang and Hojoon Lee and Jaegul Choo},
+  journal={arXiv preprint arXiv:2510.26236},
+  year={2025},
+}
+```