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These checkpoints are released for non-commercial research use under the CC BY-NC-ND 4.0 license (Attribution-NonCommercial-NoDerivatives). Please share a few details below so we can keep a light audit trail of how the weights are used in the wild.

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World Tracing β€” Scene Model (6-layer, 504 Γ— 504, r69e)

Weights last updated: 2026-10-30 β€” r69e, iter 428 000. This is the WT-S scene model of Table 2 in the paper.

Access

The checkpoints in this repo are released under the CC BY-NC-ND 4.0 license. Downloads are gated behind a short form (basic contact info + intended use) so we can keep a light audit trail; access is granted right after you submit it. Then log in with huggingface-cli login (or set HF_TOKEN) and run any of the examples from the GitHub repo β€” the wt package picks the token up automatically and --ckpt r69e triggers a normal hf_hub_download.

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
  • Architecture: MultilayerXYZModel, 1.5 B params
  • Input: 504 Γ— 504 full-frame RGB (no alpha)
  • Output: per-layer XYZ in camera space, 6 stacked depth maps
  • Training data: synthetic indoor scene renders only (including 3D-FRONT), with 10 3D-FRONT rooms held out for evaluation. No real-world or sky data β€” pre-mask the sky externally for outdoor inputs.

For sharper geometry on in-the-wild images, see the 840 Γ— 840 variant scene-model-6layer-840 (r69l).

3D-FRONT benchmark (Table 2)

eval/3dfront_test50/ holds the ground truth of the 50 held-out 3D-FRONT frames of Table 2 (input RGB, the first 6 depth-peeled layers as camera-space XYZ normalised by the median depth, per-layer validity masks, intrinsics). It is downloaded automatically by benchmarks/3dfront/eval_3dfront.py, which evaluates this checkpoint by default and can also score your own predictions with the same protocol:

python benchmarks/3dfront/eval_3dfront.py                    # this model, mean of 8 seeds
python benchmarks/3dfront/eval_3dfront.py --pred-dir preds/  # your method
MAE RMSE AbsRel L0 CD-L1 L0 CD-L2 L0 F@0.05 All CD-L1 All CD-L2 All F@0.05
Paper (WT-S, 1 seed) 0.0102 0.0215 0.0114 0.0093 0.000204 0.9867 0.0216 0.001255 0.8951
This checkpoint (mean of 8 seeds) 0.0085 0.0194 0.0094 0.0081 0.000146 0.9925 0.0208 0.001240 0.8998

The paper numbers come from an earlier checkpoint of the same run (~100k iterations before this one). The ground-truth frames are renders of 3D-FRONT scenes and are provided for evaluation only, subject to the 3D-FRONT terms of use.

Files

File Size Format
model.pt 5.59 GB bare state_dict, float32
eval/3dfront_test50/ 272 MB 50 Γ— .npz + samples_3dfront_test50.json

EMA weights only β€” ~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 triggers huggingface_hub.hf_hub_download against this repo.

Citation

@inproceedings{zhang2026worldtracinggenerativepixelaligned,
  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},
  booktitle     = {Advances in Neural Information Processing Systems (NeurIPS)},
  year          = {2026},
  note          = {Spotlight},
  eprint        = {2606.13652},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CV},
  url           = {https://arxiv.org/abs/2606.13652}
}

License

CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives) β€” see the GitHub repo. Non-commercial research use only.

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