Datasets:
| language: | |
| - en | |
| license: apache-2.0 | |
| size_categories: | |
| - 10K<n<100K | |
| pretty_name: st3d | |
| task_categories: | |
| - text-to-image | |
| tags: | |
| - 3D-layout | |
| - controllable-generation | |
| configs: | |
| - config_name: default | |
| data_files: | |
| - split: train | |
| path: train.jsonl | |
| # SeeThrough3D Dataset | |
| [**Project Page**](https://seethrough3d.github.io/) | [**Paper**](https://huggingface.co/papers/2602.23359) | [**GitHub**](https://github.com/va1bhavagrawal/seethrough3d) | |
| This is the training dataset for the CVPR 2026 🎉 paper **SeeThrough3D: Occlusion Aware 3D-Control in Text-to-Image Generation**. | |
| SeeThrough3D is a model for 3D layout-conditioned generation that explicitly models occlusions. This dataset consists of diverse multi-object scenes with strong inter-object occlusions, using an occlusion-aware 3D scene representation (OSCR) where objects are depicted as translucent 3D boxes. | |
| ## Dataset Information | |
| The primary training data is contained in `train.jsonl`. | |
| The training code expects shuffled versions of the jsonls (`train_shuffled{0..3}.jsonl`). These files are shuffled versions of `train.jsonl` with no additional content. | |
| For detailed instructions on environment setup and training, please refer to the [official GitHub repository](https://github.com/va1bhavagrawal/seethrough3d). | |
| ## Citation | |
| If you find this work or dataset useful, please cite: | |
| ```bibtex | |
| @misc{agrawal2026seethrough3docclusionaware3d, | |
| title={SeeThrough3D: Occlusion Aware 3D Control in Text-to-Image Generation}, | |
| author={Vaibhav Agrawal and Rishubh Parihar and Pradhaan Bhat and Ravi Kiran Sarvadevabhatla and R. Venkatesh Babu}, | |
| year={2026}, | |
| eprint={2602.23359}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV}, | |
| url={https://arxiv.org/abs/2602.23359}, | |
| } | |
| ``` |