README.md

---
license: mit
tags:
  - 3d
  - depth-estimation
  - multilayer-depth
  - point-cloud
  - diffusion
  - scene-understanding
library_name: torch
pipeline_tag: image-to-3d
extra_gated_heading: "Request access to the World Tracing scene model"
extra_gated_description: >
  These checkpoints are released for research and product
  experimentation under the **MIT license**.  Please share a few
  details below so we can keep a light audit trail of how the
  weights are used in the wild.  Requests are reviewed manually,
  typically within **1-3 business days**.
extra_gated_button_content: "Submit access request"
extra_gated_fields:
  Full name: text
  Affiliation (university / company): text
  Country: country
  Primary intended use:
    type: select
    options:
      - Academic research
      - Personal / hobbyist project
      - Industrial research
      - Commercial product
      - Other
  Brief description of your intended use: text
  I agree to cite the World Tracing paper in any publication or release that uses these weights: checkbox
---

# World Tracing — Scene Model (6-layer, r69e)


## Access

The checkpoints in this repo are released under the **MIT license**,
but downloads are **gated** so we can keep a light audit trail of
how the model is used.  To download:

1. Scroll up and fill in the **"Submit access request"** form (basic
   contact info + a short note on intended use).
2. We review every request manually, usually within **1-3 business
   days**.  You will receive an email from Hugging Face once your
   request is approved.
3. After approval, log in with `huggingface-cli login` (or set
   `HF_TOKEN`) and run any of the inference examples from the
   [GitHub repo](https://github.com/haoz19/world-tracing) — the `wt`
   package picks the token up automatically and `--ckpt r75b` /
   `r69e` / `r76` triggers a normal `hf_hub_download`.

> *Note:* this is a **manual review** flow, not an auto-approve
> click-through.  We read every request individually, so please give
> a one-line description of what you plan to use the weights for.


EMA-only release weights for the **r69e** scene model from
[*World Tracing: Generative Pixel-Aligned Geometry Beyond the Visible*](https://haoz19.github.io/world-tracing-page/).

* **Repo**: <https://github.com/haoz19/world-tracing>
* **Project page**: <https://haoz19.github.io/world-tracing-page/>
* **Config name**: `r69e` (and `r69g`, same architecture, different
  XYZ normalisation mode)
* **Architecture**: `MultilayerXYZModel`, 1.5 B params
* **Input**: 504 × 504 full-frame RGB (no alpha)
* **Output**: per-layer XYZ in camera space, 6 stacked depth maps
* **Training data**: Evermotion + IT-Happy indoor renders.  The model
  is trained on **indoor renders without sky** — pre-mask the sky
  externally for outdoor inputs.

## Files

| File | Size | Format |
|---|---|---|
| `model.pt` | 5.59 GB | bare `state_dict`, float32 |

EMA weights only — ~26 % of the original training checkpoint.

## Usage

```bash
git clone https://github.com/haoz19/world-tracing
cd world-tracing
pip install -e ".[viz]"

python examples/infer_scene.py \
    --image  examples/test_images/scene/scene_indoor_01_modern_living_room__seed42.png \
    --ckpt   r69e \
    --config r69e \
    --out    /tmp/wt_scene.rrd
```

Bare `--ckpt r69e` (or `--ckpt r69g`) triggers
`huggingface_hub.hf_hub_download` against this repo.

## Citation

```bibtex
@misc{zhang2026worldtracing,
  title         = {World Tracing: Generative Pixel-Aligned Geometry Beyond the Visible},
  author        = {Hao Zhang and Mohamed El Banani and Jen-Hao Cheng and Paul Zhang
                   and Yi Hua and Ben Mildenhall and Christoph Lassner
                   and Narendra Ahuja and Gengshan Yang},
  year          = {2026},
  eprint        = {TODO},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV}
}
```

## License

MIT — see the [GitHub repo](https://github.com/haoz19/world-tracing/blob/main/LICENSE).