Datasets:
metadata
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, 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.
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:
@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}
}