---
license: cc-by-nc-4.0
---
# pix2gestalt Model Weights
[Code](https://github.com/cvlab-columbia/pix2gestalt), [Website](https://gestalt.cs.columbia.edu/), [arXiv](https://arxiv.org/abs/2401.14398)
[pix2gestalt: Amodal Segmentation by Synthesizing Wholes](https://gestalt.cs.columbia.edu/)
[Ege Ozguroglu](https://egeozguroglu.github.io/)1, [Ruoshi Liu](https://ruoshiliu.github.io/)1, [Dídac Surís](https://www.didacsuris.com/)1, [Dian Chen](https://scholar.google.com/citations?user=zdAyna8AAAAJ&hl=en)2, [Achal Dave](https://www.achaldave.com/)2, [Pavel Tokmakov](https://pvtokmakov.github.io/home/)2, [Carl Vondrick](https://www.cs.columbia.edu/~vondrick/)1
1Columbia University, 2Toyota Research Institute
pix2gestalt synthesizes whole objects from only partially visible ones, enabling amodal segmentation, recognition, and 3D reconstruction of occluded objects.
## Citation
```
@misc{ozguroglu2024pix2gestalt,
title={pix2gestalt: Amodal Segmentation by Synthesizing Wholes},
author={Ege Ozguroglu and Ruoshi Liu and Dídac Surís and Dian Chen and Achal Dave and Pavel Tokmakov and Carl Vondrick},
year={2024},
eprint={2401.14398},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## Acknowledgement
This research is based on work partially supported by the Toyota Research Institute, the DARPA MCS program under Federal Agreement No. N660011924032, the NSF NRI Award \#1925157, and the NSF AI Institute for Artificial and Natural Intelligence Award \#2229929. DS is supported by the Microsoft PhD Fellowship.