metadata
license: cc-by-nc-4.0
library_name: pytorch
pipeline_tag: image-feature-extraction
tags:
- person-re-identification
- aerial-ground
- vision-transformer
- neurips2026
3D-VITAL
Checkpoints of 3D-VITAL: Visibility-aware Identity Training from Artificial Liftings of 3D Reconstruction model for Cross-View Object Re-Identification (NeurIPS 2026): arXiv:2610.16692, poster. Code, training and evaluation are at https://github.com/TurtleSmoke/3D-VITAL.
Checkpoints
Each checkpoint is trained on one camera type of AG-ReID, given by its name.
| Checkpoint | Query → Gallery | mAP | Rank-1 |
|---|---|---|---|
agreid_aerial.pth |
A → G | 52.09 | 66.20 |
| G → A | 53.30 | 62.68 | |
agreid_ground.pth |
A → G | 55.99 | 67.13 |
| G → A | 57.93 | 68.61 |
A = aerial, G = ground. The checkpoints are stored in fp16, so they can differ slightly from the paper.
Usage
git clone https://github.com/TurtleSmoke/3D-VITAL && cd 3D-VITAL && uv sync && source .venv/bin/activate
hf download TurtleSmoke/3D-VITAL --include "*.pth" --local-dir checkpoints
bash scripts/eval.sh agreid_aerial $DATA checkpoints/agreid_aerial.pth
License
The weights are released for non-commercial research under CC BY-NC 4.0. They were trained on AG-ReID and on synthetic views generated with Hunyuan3D-2 and StyleID, whose respective terms also apply. The code is under the CeCILL-B license.
Citation
@inproceedings{grolleau2026vital,
title = {3D-VITAL: Visibility-aware Identity Training from Artificial Liftings of 3D Reconstruction model for Cross-View Object Re-Identification},
author = {Grolleau, William and Sabourin, Astrid and Lapouge, Guillaume and Achard, Catherine},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
year = {2026}
}