Synthetic foot dataset used for foot prediction tasks. As featured in:

FOUND: Foot Optimisation with Uncertain Normals for Surface Deformation using Synthetic Data
Winter Conference on Applications of Computer Vision 2024
Oliver Boyne, Gwangbin Bae, James Charles, and Roberto Cipolla
[arXiv] [project page]

FOCUS: Multi-View Foot Reconstruction from Synthetically Trained Dense Correspondences
3DV 2025
Oliver Boyne and Roberto Cipolla
[arXiv] [project page]

Data was produced using our BlenderSynth package.

Install

Dataset	Description	Download Link	Readme
V1	50K RGB, normals, masks & keypoints	Download	readme
V2	100K RGB, normals, masks, and dense correspondences	Download	readme

Normal format

Our normals are formatted in a camera relative reference frame, with RGB corresponding to XYZ, normalized in 0-255, such that (0, 1, 0) -> (128, 255, 128).

In our format, (XYZ) = (left, up, back)

Citation

If you use our work, please cite:

@inproceedings{boyne2024found,
            title={FOUND: {F}oot {O}ptimisation with {U}ncertain {N}ormals for Surface {D}eformation using Synthetic Data},
            author={Boyne, Oliver, and Bae, Gwangbin, and Charles, James and Cipolla, Roberto},
            booktitle={Winter Conference on Applications of Computer Vision (WACV)},
            year={2024}
}

@inproceedings{boyne2025focus,
            title={FOCUS: Multi-View Foot Reconstruction from Synthetically Trained Dense Correspondences},
            author={Boyne, Oliver and Cipolla, Roberto},
            booktitle={2025 International Conference on 3D Vision (3DV)},
            year={2025}
}