Datasets:
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pretty_name: AmbiSem-3D
tags:
- 3d
- image
- amodal-3d-generation
- semantic-ambiguity
---
# AmbiSem-3D
AmbiSem-3D is a diagnostic benchmark for amodal 3D generation under semantic
ambiguity, introduced with [**RelaxFlow: Text-Driven Amodal 3D
Generation**](https://arxiv.org/abs/2603.05425), an ICML 2026 Spotlight paper.
This repository contains two separated subsets:
| Subset | Folder | Entries | Description |
|---|---:|---:|---|
| Original | `original/` | 21 | Hand-curated single-image ambiguity cases |
| Extended | `extended/` | 100 | Semi-automatically curated view-induced ambiguity cases |
Each subset has its own `manifest.json`, `README.md`, and `assets/` directory.
All paths in a subset manifest are relative to that subset folder.
Code and runners are available in the
[**RelaxFlow repository**](https://github.com/viridityzhu/RelaxFlow).
## Usage
Download the repository, then pass the subset manifest path to the RelaxFlow
batch runner or to the `prepare_manifest_with_priors.py` helper from the
RelaxFlow code release.
## Citation
If you use AmbiSem-3D or RelaxFlow, please cite:
```bibtex
@inproceedings{zhu2026relaxflow,
title = {RelaxFlow: Text-Driven Amodal 3D Generation},
author = {Zhu, Jiayin and Fu, Guoji and Liu, Xiaolu and He, Qiyuan and Li, Yicong and Yao, Angela},
booktitle = {Proceedings of the 43rd International Conference on Machine Learning},
year = {2026},
url = {https://arxiv.org/abs/2603.05425}
}
```
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