Init README.md

README.md

@@ -1,3 +1,57 @@
 ---
 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}
+}
+```