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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.