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teemosliang-sdpose-wholebody

Name: teemosliang-sdpose-wholebody
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top-down-pose-estimation

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SDPose: Exploiting Diffusion Priors for Out-of-Domain and Robust Pose Estimation (WholeBody - 133 Keypoints)

Paper

Project Page

License: MIT

Model Description

SDPose is a state-of-the-art human pose estimation model that leverages the powerful visual priors from Stable Diffusion to achieve exceptional performance on out-of-distribution (OOD) scenarios. This model variant estimates 133 wholebody keypoints, including body, hands, face, feet.

Model Architecture

SDPose employs a U-Net backbone initialized with Stable Diffusion v2 weights, combined with a specialized heatmap head for keypoint prediction. The model operates in a top-down manner:

Person Detection: Detect human bounding boxes using an object detector (e.g., YOLO11-x)
Pose Estimation: Crop and estimate 17 body keypoints for each detected person
Heatmap Generation: Produce confidence heatmaps for precise keypoint estimation

Model Specifications:

Backbone: Stable Diffusion v2 U-Net (fine-tuned; minimal architectural changes)
Head: Custom heatmap prediction head
Input Resolution: 1024×768 (H×W)
Output: 133 keypoint heatmaps + coordinates with confidence scores
Framework: MMPose

Supported Keypoints (COCO Wholebody Format)

The model predicts 133 body keypoints following the COCO Wholebody keypoint format.

Intended Use

Human pose estimation in natural images
Pose estimation in artistic and stylized domains (paintings, anime, sketches)
Animation and video pose tracking
Cross-domain pose analysis and research
Applications requiring robust pose estimation under distribution shifts

How to Use

Installation

bash

# Clone the repository
git clone https://github.com/t-s-liang/SDPose-OOD.git
cd SDPose-OOD

# Install dependencies
pip install -r requirements.txt
# Download YOLO11-x for human detection
wget https://github.com/ultralytics/assets/releases/download/v8.3.0/yolo11x.pt -P models/

# Launch Gradio interface
cd gradio_app
bash launch_gradio.sh

Training Data

Datasets

Trained exclusively on COCO-2017 train2017 (no extra data).

COCO-Wholebody (Common Objects in Context): 200K+ images with 133 wholebody keypoints

Preprocessing

Images are resized and cropped to 1024×768 resolution
Augmentation: random horizontal flip, half-body & bbox transforms, UDP affine; Albumentations (Gaussian/Median blur, coarse dropout).
Heatmaps: UDP codec (MMPose style).

Comparison with Baselines

SDPose significantly outperforms traditional pose estimation models (e.g., Sapiens) on out-of-distribution benchmarks while maintaining competitive performance on in-domain data.

See our paper for comprehensive evaluation results.

Citation

If you use SDPose in your research, please cite our paper:

bibtex

@misc{liang2025sdposeexploitingdiffusionpriors,
      title={SDPose: Exploiting Diffusion Priors for Out-of-Domain and Robust Pose Estimation}, 
      author={Shuang Liang and Jing He and Chuanmeizhi Wang and Lejun Liao and Guo Zhang and Yingcong Chen and Yuan Yuan},
      year={2025},
      eprint={2509.24980},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2509.24980}, 
}

License

This model is released under the MIT License.

Additional Resources

🌐 Project Website: https://t-s-liang.github.io/SDPose
📄 Paper: arXiv:2509.24980
💻 Code Repository: GitHub
🤗 Demo: HuggingFace Space
📧 Contact: tsliang2001@gmail.com

⭐ Star us on GitHub — it motivates us a lot!

fwwrsd/teemosliang-sdpose-wholebody