ExtremeOcc-3D

ExtremeOcc-3D is a benchmark for amodal 3D generation under severe object occlusion, introduced with RelaxFlow: Text-Driven Amodal 3D Generation, 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.

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
    └── ...

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:

@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}
}