original/README.md

---
pretty_name: "AmbiSem-3D"
tags:
- 3d
- image
- amodal-3d-generation
- semantic-ambiguity
---

# AmbiSem-3D

A diagnostic set of 21 single-image cases for amodal 3D generation under semantic ambiguity, where the visible evidence in an image is ambiguous, consistent with multiple plausible object identities. Each sample provides an observation image, a category text label disambiguating the intended interpretation, and (for some samples) an object mask.

## Folder layout

```
AmbiSem-3D/
├── manifest.json           # 21 sample entries
└── assets/
    └── <sample_id>/
        ├── input.png       # observation image
        └── mask.png        # optional, only for samples that include one
```

## Manifest fields

Each entry contains:

| Field | Description |
|---|---|
| `id` | Stable sample identifier matching the `assets/<sample_id>/` folder |
| `image` | Observation image (relative to dataset root) |
| `obs_object_image` | Same as `image` for this dataset |
| `prior_text` | Disambiguating text caption |
| `mask` | Object mask, included only when available |

All paths are relative to this `AmbiSem-3D/` 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/manifest.json \
  --priors-root path/to/my_priors \
  --output path/to/AmbiSem-3D/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 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}
}
```