| --- |
| 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} |
| } |
| ``` |
|
|
