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pretty_name: "ExtremeOcc-3D"
tags:
- 3d
- image
- amodal-3d-generation
- extreme-occlusion
---
# ExtremeOcc-3D
ExtremeOcc-3D is a benchmark for amodal 3D generation under severe object
occlusion, introduced with [**RelaxFlow: Text-Driven Amodal 3D
Generation**](https://arxiv.org/abs/2603.05425), an ICML 2026 Spotlight paper.
The benchmark contains 264 single-view indoor scenes. Each sample provides an
occluded scene render, a target-object mask, an isolated render of the visible
object part, a category text label, and ground-truth 3D assets for evaluation.
Use `manifest.json` as the entry point. All paths in the manifest are relative
to this dataset root. Code and runners are available in the
[**RelaxFlow repository**](https://github.com/viridityzhu/RelaxFlow).
## Folder layout
```
ExtremeOcc-3D/
├── manifest.json # 264 sample entries
├── assets/ # observation images and masks per sample
│ └── <scene>/<view>/{rendered_scene.png, occluded_mask.png, rendered.png, ...}
└── gt_assets/ # ground-truth assets shared by views of the same object
└── <object_hash>/{mesh.ply, view_0000.png, ...}
```
## Manifest
Each entry contains:
| Field | Description |
|---|---|
| `id` | Stable sample identifier |
| `image` | Occluded scene render (relative to dataset root) |
| `mask` | Object mask for the target object |
| `obs_object_image` | Isolated render of the observed (visible) part of the object |
| `prior_text` | Object category label |
| `gt_render_dir` | Directory of multi-view GT renders for the target object |
| `gt_mesh` | Ground-truth mesh (`.ply`) for the target object |
All paths are relative to this `ExtremeOcc-3D/` directory.
## Usage
Download this repository, then pass `manifest.json` to the RelaxFlow batch
runner. If you generate prior images separately, use
`prepare_manifest_with_priors.py` from the RelaxFlow release to attach them to a
copy of the manifest.
## 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/ExtremeOcc-3D/manifest.json \
--priors-root path/to/my_priors \
--output path/to/ExtremeOcc-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 ExtremeOcc-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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