Add WNM-3D model card

README.md

@@ -1,3 +1,107 @@
 ---
 license: fair-noncommercial-research-license
+tags:
+  - robotics
+  - vision-language-navigation
+  - embodied-ai
+  - world-model
+  - video-generation
+  - pytorch
 ---
+
+# WNM-3D
+
+WNM-3D is a generative world navigation model for continuous
+vision-language navigation. It converts monocular egocentric RGB history into
+persistent geometry-aware scene tokens and jointly generates future views and
+navigation actions for closed-loop control.
+
+- [Project page](https://wnm-3d.github.io/)
+- [Paper](https://arxiv.org/abs/2608.07267)
+- [Source code and documentation](https://github.com/TeleHuman/WNM-3D)
+- [GN0 / GN-Bench](https://github.com/TeleHuman/GN0)
+- [GN-Matrix dataset](https://huggingface.co/datasets/TeleEmbodied/GN-Matrix)
+
+## Released Checkpoints
+
+| Directory | Training stage | Recommended use |
+| --- | --- | --- |
+| `wnm_3d_stage1_release` | Offline A\* SFT | Stage-I analysis and initialization |
+| `wnm_3d_stage2_release` | Closed-loop DAgger-SFT | Stage-II analysis and initialization |
+| `wnm_3d_stage3_release` | Counterfactual DanceGRPO | Evaluation and inference |
+
+The Stage-III checkpoint is the primary released policy. Each directory is a
+self-contained inference checkpoint; standalone Wan, UMT5-XXL, and VGGT-Ω
+initialization weights are not required for evaluation.
+
+## Download
+
+```bash
+hf download TeleEmbodied/WNM-3D \
+  --include "wnm_3d_stage3_release/**" \
+  --local-dir checkpoints
+```
+
+## Inference
+
+Install WNM-3D by following the
+[installation guide](https://github.com/TeleHuman/WNM-3D/blob/main/docs/INSTALLATION.md),
+then launch a single policy replica:
+
+```bash
+bash scripts/inference/wnm_3d_server.sh \
+  --model-path checkpoints/wnm_3d_stage3_release \
+  --cuda-devices 0 \
+  --num-replicas 1 \
+  --base-port 8000
+```
+
+Run the GN-Bench client from a sibling GN0 checkout:
+
+```bash
+cd ../GN0
+bash scripts/evaluation/eval_remote.sh \
+  --exp-config configs/gn_bench/interiorgs/test_unseen.yaml \
+  --enable-stall-recovery \
+  --num-gpus 1 \
+  --result-dir tmp/eval/wnm_3d_test_unseen
+```
+
+The reference single-replica deployment was validated on an NVIDIA H100 80 GB
+GPU and used approximately 27 GiB of GPU memory after loading. A released
+checkpoint occupies approximately 26 GiB. These figures are observations from
+the reference configuration, not strict minimum requirements.
+
+## Intended Use and Limitations
+
+WNM-3D is intended for research on embodied navigation, world models, and
+closed-loop vision-language navigation. The released policy was developed and
+evaluated with the InteriorGS scenes and GN-Matrix task annotations used by
+GN-Bench. Performance may not transfer to new simulators, sensors, scene
+distributions, languages, or physical robots without additional validation.
+
+Generated actions can fail or behave unexpectedly. Do not use the model as a
+safety-critical controller or deploy it around people, property, or physical
+systems without appropriate safeguards and human oversight.
+
+## License
+
+The model repository is published under the FAIR Noncommercial Research
+License shown in its Hugging Face metadata. The WNM-3D source repository
+contains both Apache-2.0 portions and vendored components governed by separate
+terms, including the FAIR Noncommercial Research License and DINOv3 License.
+Review the complete
+[license boundaries and notices](https://github.com/TeleHuman/WNM-3D/blob/main/docs/THIRD_PARTY_NOTICES.md)
+before use or redistribution. Datasets and upstream initialization weights are
+subject to their providers' terms.
+
+## Citation
+
+```bibtex
+@article{huang2026wnm_3d,
+  title={WNM-3D: A World Navigation Model with 3D Scene Conditioning for Closed-Loop VLN},
+  author={Huang, Yuehao and Wu, Yunzi and Zhang, Xiaotao and Li, Xinhai and Dong, Jiankun and Lv, Jiajun and Zhang, Chi and Bai, Chenjia and Liu, Yong and Li, Xuelong},
+  journal={arXiv preprint arXiv:2608.07267},
+  year={2026}
+}
+```