Datasets:
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pretty_name: "AmbiSem-3D-Ext"
tags:
- 3d
- image
- amodal-3d-generation
- semantic-ambiguity
---
# AmbiSem-3D-Ext
A semi-automatically curated extended set of 100 single-image cases for amodal 3D generation under view-induced semantic ambiguity. Each sample is a single rendered view of a 3D object whose appearance, from this particular viewpoint, plausibly admits multiple interpretations. Every entry comes with the observation image and a disambiguating text caption indicating the intended interpretation.
## Folder layout
```
AmbiSem-3D-Ext/
├── manifest.json # 100 sample entries
└── assets/
└── <sample_id>/
└── ambiguous_view.png
```
`<sample_id>` encodes the source object hash and the rendered view index.
## Manifest fields
Each entry contains:
| Field | Description |
|---|---|
| `id` | Stable sample identifier matching the `assets/<sample_id>/` folder |
| `image` | Observation image (the ambiguous view) |
| `obs_object_image` | Same as `image` for this dataset |
| `prior_text` | Disambiguating text caption |
All paths are relative to this `AmbiSem-3D-Ext/` directory.
## Adding prior images
The manifest does not include a `prior_images` field. Generate priors with any image generator you prefer, organize them per sample, then run the helper to produce a complete manifest:
```
my_priors/
└── <sample_id>/ # must match the `id` field in manifest.json
├── prior_0.png
├── prior_1.png # multiple priors are supported
└── ...
```
```bash
python prepare_manifest_with_priors.py \
--manifest path/to/AmbiSem-3D-Ext/manifest.json \
--priors-root path/to/my_priors \
--output path/to/AmbiSem-3D-Ext/manifest_with_priors.json
```
Accepted image extensions: `.png .jpg .jpeg .webp`. Files inside each `<sample_id>/` are picked up in sorted order. The output `manifest_with_priors.json` plugs directly into the relaxflow batch runners.
## Citation
If you use AmbiSem-3D-Ext 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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