---
license: cc-by-4.0
task_categories:
- image-text-to-image
language:
- en
tags:
- image-editing
- instruction-based-editing
- image-generation
- ABO-Edit
size_categories:
- 10K<n<100K
---

# DiTailed: Ensuring Visual Object Consistency in Text-Image-to-Image Flow Matching Models

We present **ABO-Edit**, a curated dataset for *training* and *evaluating* generative models on *Visual Object Consistency*. 
ABO-Edit addresses the challenging task of transforming “Lifestyle” images (depicting products in complex real-world usage scenarios) into studio-quality representations: the same product *isolated on a white
background with realistic shadow, rotated and tilted to a precisely specified angle.*

Each sample comprises a triplet \\( \langle x_{src},\ p_{src \rightarrow trg},\ x_{trg} \rangle \\), where:

1. \\(x_{src}\\) denotes a source lifestyle image
2. \\( p_{src \rightarrow trg} \\) represents a detailed editing prompt including fine-grained rotation
angles
3. \\( x_{trg} \\) is the corresponding ground-truth target image rendered from a 3D asset

## Attribution & License
- **Original data credit:** Images and 3D assets are © Amazon.com.
- **Modifications:** This dataset was constructed on top of ABO by rendering studio-quality
  target images from ABO 3D assets and pairing them with lifestyle sources and VLM-generated editing prompts;
  see our paper for full details.
  
In accordance with CC BY 4.0, **ABO-Edit** is derived from [Amazon Berkeley Objects (ABO)](https://amazon-berkeley-objects.s3.amazonaws.com/index.html) and it is distributed under the same **CC BY 4.0** license,
and no additional restrictions are applied.

## Citation
If you use this dataset, please cite **both** our work and the original ABO dataset.
```bibtex
@misc{taioli2026ABO-Edit,
  title={DiTailed: Ensuring Visual Object Consistency in Text-Image-to-Image Flow Matching Models}, 
  author={Francesco Taioli and Daniel Coelho and Iaroslav Melekhov and Roberto Alcover-Couso and Jose Miguel Grande Saiz and Virginia Fernandez Arguedas and Artur Bekasov},
  year={2026},
  eprint={2607.12539},
  archivePrefix={arXiv},
  primaryClass={cs.CV},
  ]url={https://arxiv.org/abs/2607.12539}, 
}

@article{collins2022abo,
  title={ABO: Dataset and Benchmarks for Real-World 3D Object Understanding},
  author={Collins, Jasmine and Goel, Shubham and Deng, Kenan and Luthra, Achleshwar and Xu, Leon and Gundogdu, Erhan and Zhang, Xi and Yago Vicente, Tomas F and Dideriksen, Thomas and Arora, Himanshu and Guillaumin, Matthieu and Malik, Jitendra},
  journal={CVPR},
  year={2022}
}
```