Improve dataset card: Add task categories, HF paper link, sample usage, and dataset details

README.md

@@ -1,5 +1,11 @@
 ---
 license: apache-2.0
+task_categories:
+- image-to-video
+tags:
+- human-image-animation
+- video-generation
+- pose-guided
 ---
 
 <p align="center">
@@ -30,17 +36,18 @@ license: apache-2.0
     <br>
     <br>
         <a href="https://arxiv.org/abs/2511.19320"><img src='https://img.shields.io/badge/arXiv-2511.19320-red' alt='Paper PDF'></a>
+        <a href="https://huggingface.co/papers/2511.19320"><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Paper-orange' alt='Hugging Face Paper'></a>
         <a href='https://mcg-nju.github.io/steadydancer-web'><img src='https://img.shields.io/badge/Project-Page-blue' alt='Project Page'></a>
         <a href='https://github.com/MCG-NJU/SteadyDancer'><img src='https://img.shields.io/badge/Github-SteadyDancer-orange'></a>
         <a href='https://huggingface.co/MCG-NJU/SteadyDancer-14B'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Model-yellow'></a>
         <a href='https://huggingface.co/datasets/MCG-NJU/X-Dance'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-X--Dance-green'></a>
     <br>
-    <b></a>Multimedia Computing Group, Nanjing University &nbsp; | &nbsp; </a>Platform and Content Group (PCG), Tencent  </b>
+    <b>Multimedia Computing Group, Nanjing University &nbsp; | &nbsp; Platform and Content Group (PCG), Tencent  </b>
     <br>
   </p>
 </p>
 
-This repository is the `test dataset` of paper "SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation", called **X-Dance**.
+This repository is the `test dataset` of paper "[SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation](https://huggingface.co/papers/2511.19320)", called **X-Dance**.
 
 SteadyDancer is a strong animation framework based on **Image-to-Video paradigm**, ensuring **robust first-frame preservation**. In contrast to prior *Reference-to-Video* approaches that often suffer from identity drift due to **spatio-temporal misalignments** common in real-world applications, SteadyDancer generates **high-fidelity and temporally coherent** human animations, outperforming existing methods in visual quality and control while **requiring significantly fewer training resources**.
 
@@ -50,4 +57,175 @@ We first collected 12 distinct driving videos, comprising 8 sequences of intrica
 Tailored to these motions, **we specifically curated a diverse set of reference images to simulate real-world misalignments**. This specially designed collection contains: (1) anime characters to introduce stylistic domain gaps; (2) half-body shots to represent compositional inconsistencies; (3) cross-gender or anime characters to simulate significant skeletal structural discrepancies; and (4) subjects in distinct postures to maximize the initial action gap.
 By systematically pairing these reference images with the 12 driving videos, we simulate two critical real-world challenges: (1) Spatial pose-structure inconsistency (e.g., an anime character driving a real-world pose); and (2) Temporal discontinuity, specifically the significant gap between the reference pose and the initial driving pose.
 
-![X-Dance](assets/X-Dance.png)
+![X-Dance](https://cdn-uploads.huggingface.co/production/uploads/6667e3d60a7f1d1cbb63cf4d/y0853pM3f0-4NqR0G2i_Q.png)
+
+## Sample Usage
+
+To generate dance video from a source image and a driving video using the SteadyDancer model with this dataset, please follow the steps below from the [official GitHub repository](https://github.com/MCG-NJU/SteadyDancer).
+
+### 🛠️ Installation
+```bash
+# Clone this repository
+git clone https://github.com/MCG-NJU/SteadyDancer.git
+cd SteadyDancer
+
+# Create and activate conda environment
+conda create -n steadydancer python=3.10 -y
+conda activate steadydancer
+
+# Install animate generation dependencies (Pytorch 2.5.1, CUDA 12.1 for example)
+pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu121
+pip install https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.4.post1/flash_attn-2.7.4.post1+cu12torch2.5cxx11abiFALSE-cp310-cp310-linux_x86_64.whl && python -c "import flash_attn"
+pip install xformers==0.0.29.post1
+pip install "xfuser[diffusers,flash-attn]"
+pip install -r requirements.txt
+
+# Install pose extraction dependencies
+pip install moviepy decord              # moviepy-2.2.1, decord-0.6.0
+pip install --no-cache-dir -U openmim   # openmim-0.3.9
+mim install mmengine                    # mmengine-0.10.7
+mim install "mmcv==2.1.0"               # mmcv-2.1.0
+mim install "mmdet>=3.1.0"              # mmdet-3.3.0
+pip install mmpose                      # mmpose-1.3.2
+```
+
+- Errors consistently occur during the installation of the mmcv and mmpose packages, so please verify that both packages were installed successfully:
+```bash
+python -c "import mmcv"
+python -c "import mmpose"
+python -c "from mmpose.apis import inference_topdown"
+python -c "from mmpose.apis import init_model as init_pose_estimator"
+python -c "from mmpose.evaluation.functional import nms"
+python -c "from mmpose.utils import adapt_mmdet_pipeline"
+python -c "from mmpose.structures import merge_data_samples"
+```
+
+- If you encounter "*ModuleNotFoundError: No module named 'mmcv._ext'*" issue during installation, please re-install mmcv manually (We haven't found a more convenient and stable method. If you have a better method, please submit a pull request to help us. We would greatly appreciate it 😊.):
+```bash
+mim uninstall mmcv -y
+git clone https://github.com/open-mmlab/mmcv.git
+cd mmcv && git checkout v2.1.0
+pip install -r requirements/optional.txt
+gcc --version                               # Check the gcc version (requires 5.4+)
+python setup.py build_ext                   # Build the C++ and CUDA extensions, may take a while
+python setup.py develop
+pip install -e . -v                         # Install mmcv in editable mode
+python .dev_scripts/check_installation.py   # just verify the installation was successful by running this script, ignore the last verify script
+cd ../
+```
+
+### 📥 Download Checkpoints
+```bash
+# Download DW-Pose pretrained weights
+mkdir -p ./preprocess/pretrained_weights/dwpose
+huggingface-cli download yzd-v/DWPose --local-dir ./preprocess/pretrained_weights/dwpose --include "dw-ll_ucoco_384.pth"
+wget https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth -O ./preprocess/pretrained_weights/dwpose/yolox_l_8x8_300e_coco.pth
+
+# Download SteadyDancer-14B model weights
+huggingface-cli download jiamingZ/SteadyDancer-14B --local-dir ./SteadyDancer-14B
+```
+
+### 🚀 Inference
+
+To generate dance video from a source image and a driving video (We have provided pose example in `preprocess/output/video00001_img00001/example` and `preprocess/output/video00002_img00002/example` to try our model quickly), please follow the steps below:
+- Pose extraction and alignment:
+```bash
+ref_image_path="data/images/00001.png"
+driving_video_path="data/videos/00001"
+pair_id="video00001_img00001"
+output=./preprocess/output/${pair_id}/$(date +"%Y%m%d%H%M%S")
+
+## Extract and align pose (Positive Condition)
+outfn=$output/positive/all.mp4
+outfn_align_pose_video=$output/positive/single.mp4
+python preprocess/pose_align.py \
+    --imgfn_refer "$ref_image_path" \
+    --vidfn "${driving_video_path}/video.mp4" \
+    --outfn "$outfn" \
+    --outfn_align_pose_video "$outfn_align_pose_video"
+
+outfn_align_pose_video=$output/positive/single.mp4
+python preprocess/dump_video_images.py "$outfn_align_pose_video" "$(dirname "$outfn_align_pose_video")"
+
+
+## Extract and align pose (Negative Condition)
+outfn=$output/negative/all.mp4
+outfn_align_pose_video=$output/negative/single.mp4
+python preprocess/pose_align_withdiffaug.py \
+    --imgfn_refer "$ref_image_path" \
+    --vidfn "${driving_video_path}/video.mp4" \
+    --outfn "$outfn" \
+    --outfn_align_pose_video "$outfn_align_pose_video"
+
+outfn_align_pose_video=$output/negative/single_aug.mp4
+python preprocess/dump_video_images.py "$outfn_align_pose_video" "$(dirname "$outfn_align_pose_video")"
+
+
+## copy other files
+cp "$ref_image_path" "$output/ref_image.png"
+cp "${driving_video_path}/video.mp4" "$output/driving_video.mp4"
+cp "${driving_video_path}/prompt.txt" "$output/prompt.txt"
+
+
+## (Optional) Visualization of original pose without alignment
+driving_video_path="data/videos/00001"
+python preprocess/pose_extra.py \
+    --vidfn $driving_video_path/video.mp4 \
+    --outfn_all $driving_video_path/pose_ori_all.mp4 \
+    --outfn_single $driving_video_path/pose_ori_single.mp4
+```
+
+- Generate animation video with SteadyDancer:
+```bash
+ckpt_dir="./SteadyDancer-14B"
+
+input_dir="preprocess/output/video00001_img00001/example"   # </path/to/preprocess/output/> contains ref_image.png, driving_video.mp4, prompt.txt, positive/, negative/ folders, e.g. the above ./preprocess/output/${pair_id}/$(date +"%Y%m%d%H%M%S")
+image="$input_dir/ref_image.png"          # reference image path
+cond_pos_folder="$input_dir/positive/"    # positive condition pose folder
+cond_neg_folder="$input_dir/negative/"    # negative condition pose folder
+prompt=$(cat $input_dir/prompt.txt)       # read prompt from file
+save_file="$(basename "$(dirname "$input_dir")")---$(basename "$input_dir").mp4"  # save file name
+
+cfg_scale=5.0
+condition_guide_scale=1.0
+pro=0.4
+base_seed=106060
+
+CUDA_VISIBLE_DEVICES=0 python generate_dancer.py \
+    --task i2v-14B --size 1024*576 \
+    --ckpt_dir $ckpt_dir \
+    --prompt "$prompt" \
+    --image "$image" \
+    --cond_pos_folder "$cond_pos_folder" \
+    --cond_neg_folder "$cond_neg_folder" \
+    --sample_guide_scale $cfg_scale \
+    --condition_guide_scale $condition_guide_scale \
+    --end_cond_cfg $pro \
+    --base_seed $base_seed \
+    --save_file "$save_file"
+```
+
+## 🎥 X-Dance Benchmark
+To fill the void left by existing same-source benchmarks (such as TikTok), which fail to evaluate spatio-temporal misalignments, we propose **X-Dance**, a new benchmark that focuses on these challenges. The X-Dance benchmark is constructed from diverse image categories (male/female/cartoon, and upper-/full-body shots) and challenging driving videos (complex motions with blur and occlusion). Its curated set of pairings intentionally introduces spatial-structural inconsistencies and temporal start-gaps, allowing for a more robust evaluation of model generalization in the real world.
+You can download the X-Dance benchmark from [huggingface](https://huggingface.co/datasets/MCG-NJU/X-Dance).
+
+## ❤️ Acknowledgements
+Our implementation is based on [Wan 2.1](https://github.com/Wan-Video/Wan2.1). We modify [MusePose](https://github.com/TMElyralab/MusePose/tree/main) to generate and align pose video. Thanks for their remarkable contribution and released code!
+
+## 📚 Citation
+
+If you find our paper or this codebase useful for your research, please cite us.
+```BibTeX
+@misc{zhang2025steadydancer,
+      title={SteadyDancer: Harmonized and Coherent Human Image Animation with First-Frame Preservation}, 
+      author={Jiaming Zhang and Shengming Cao and Rui Li and Xiaotong Zhao and Yutao Cui and Xinglin Hou and Gangshan Wu and Haolan Chen and Yu Xu and Limin Wang and Kai Ma},
+      year={2025},
+      eprint={2511.19320},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2511.19320}, 
+}
+```
+
+## 📄 License
+This repository is released under the Apache-2.0 license as found in the [LICENSE](LICENSE) file.