Ilya A. Petrov Riccardo Marin Julian Chibane Gerard Pons-Moll
The code was tested under Ubuntu 22.04, Python 3.10, CUDA 11.8, PyTorch 2.0.1.
Use the following command to create a conda environment with necessary dependencies:
conda env create -f environment.ymlThe steps are described in docs/data.md.
Pre-trained models can be obtained from the link. With the commands:
wget https://nc.mlcloud.uni-tuebingen.de/index.php/s/PG8wZ5HRKytEY8S/download/object_pop_up_noclass.tar -P ./assets
wget https://nc.mlcloud.uni-tuebingen.de/index.php/s/Dfx9rfQ2tW4ZsEY/download/object_pop_up_class.tar -P ./assetsUse the following commands to run evaluation:
# model without class prediction (assumes 24GB GPU memory)
python evaluate.py scenarios/gb_PNv2_noclass.toml -b 64 -w 20 -d grab behave -g -rc ./assets/object_pop_up_noclass.pth -c configs/smplh.toml
# model with class prediction (assumes 24GB GPU memory)
python evaluate.py scenarios/gb_PNv2_class.toml -b 64 -w 20 -d grab behave -g -rc ./assets/object_pop_up_class.pth -c configs/smplh.tomlUse the following command to run the training:
python train.py scenarios/gb_PNv2_noclass.toml -b 32 -w 10 -nowb -ep 0001_smplh -c configs/smplh.toml@inproceedings{petrov2023popup,
title={Object pop-up: Can we infer 3D objects and their poses from human interactions alone?},
author={Petrov, Ilya A and Marin, Riccardo and Chibane, Julian and Pons-Moll, Gerard},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year={2023}
}This project benefited from the following resources: