CameraHMR: Aligning People with Perspective (3DV 2025)

Priyanka Patel                Michael J. Black

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🌐 Project Page | 📄 ArXiv Paper | 🎥 Video Results

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Figure: CameraHMR Results

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🚀 Release

• CameraHMR Demo Code
• CameraHMR Training and Evaluation Code

🕒 Coming Soon

• CamSMPLify Code
• HumanFoV and DenseKP Code

Installation

Create a conda environment and install all the requirements.

conda create -n camerahmr python=3.10
conda activate camerahmr
pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt

🎬 Demo

Download Required Files

Download necessary demo files using:

bash fetch_demo_data.sh

Alternatively, download files manually from the CameraHMR website. Ensure to update paths in constants.py if doing so manually.

Run the demo with following command. It will run demo on all images in the specified --image_folder, and save renderings of the reconstructions and the output mesh in --out_folder.

python demo.py --image_folder demo_images --output_folder output_images

4DHumans Labels with full perspective camera

You can download the training data; fitted SMPL parameters for INSTA/AIC/COCO/MPII images from the CameraHMR website (registration required).

Alternatively, use the following script:

bash scripts/fetch_4dhumans_training_labels.sh

Note: We cannot provide the original AIC/INSTA images. These images must be obtained from their original sources. For convenience, you can use the 4D-Humans repository, which offers these images in WebDataset format. To extract images from the WebDataset, refer to this script.

To overlay the fitted SMPL mesh on your images, use the following command:

python dataset_vis.py --image_folder path_to_img_folder --output_folder path_for_output_file --npz_path path_to_npz_file

path_to_img_folder corresponds to path of download INSTA/AIC images. path_to_npz_file corresponds to downloaded SMPL params.

Training and Evaluation

Please check out the document for Training and Evaluation detail instructions.

🙌 Acknowledgements

This project leverages outstanding resources from:

• 4D-Humans
• BEDLAM
• SMPLify
• ViTPose
• Detectron2

📚 Citation

If you find CameraHMR useful in your work, please cite:

@article{patel2024camerahmr,
  title={CameraHMR: Aligning People with Perspective},
  author={Patel, Priyanka and Black, Michael J},
  journal={arXiv preprint arXiv:2411.08128},
  year={2024}
}

✨ Thank you for your interest in CameraHMR! ✨