Request access to the World Tracing scene model

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.

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:

Scroll up and fill in the "Submit access request" form (basic contact info + a short note on intended use).
We review every request manually, usually within 1-3 business days. You will receive an email from Hugging Face once your request is approved.
After approval, log in with huggingface-cli login (or set HF_TOKEN) and run any of the inference examples from the GitHub repo — 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.

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

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

@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.

Downloads last month: -; Downloads are not tracked for this model. How to track

Inference Providers NEW

Image-to-3D

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support