Add pipeline tag, paper link, citation (incorporating HF community PR #1)

README.md

@@ -1,5 +1,6 @@
 ---
 license: apache-2.0
+pipeline_tag: image-to-3d
 tags:
 - novel-view-synthesis
 - multi-view-diffusion
@@ -11,7 +12,9 @@ tags:
 
 **Repurposing Geometric Foundation Models for Multi-view Diffusion**
 
-[[Project Page]](https://cvlab-kaist.github.io/GLD/) | [[Code]](https://github.com/cvlab-kaist/GLD) | [[arXiv]](https://arxiv.org/abs/2603.22275)
+[[Paper]](https://huggingface.co/papers/2603.22275) | [[arXiv]](https://arxiv.org/abs/2603.22275) | [[Project Page]](https://cvlab-kaist.github.io/GLD/) | [[Code]](https://github.com/cvlab-kaist/GLD)
+
+Geometric Latent Diffusion (GLD) is a framework that repurposes the geometrically consistent feature space of geometric foundation models (such as [Depth Anything 3](https://github.com/DepthAnything/Depth-Anything-3) and [VGGT](https://github.com/facebookresearch/vggt)) as the latent space for multi-view diffusion. By operating in this space rather than a view-independent VAE latent space, GLD achieves consistent novel view synthesis (NVS) and 3D reconstruction with significantly faster training convergence.
 
 ## Quick Start
 
@@ -40,3 +43,18 @@ python -c "from huggingface_hub import snapshot_download; snapshot_download('Seo
 | `pretrained_models/da3/dpt_decoder.pt` | DPT decoder (depth + geometry) | 0.4G |
 | `pretrained_models/mae_decoder.pt` | DA3 MAE decoder (RGB) | 1.6G |
 | `pretrained_models/vggt/mae_decoder.pt` | VGGT MAE decoder (RGB) | 1.6G |
+
+## Citation
+
+```bibtex
+@article{jang2026gld,
+  title={Repurposing Geometric Foundation Models for Multi-view Diffusion},
+  author={Jang, Wooseok and Jeon, Seonghu and Han, Jisang and Choi, Jinhyeok and Kwon, Minkyung and Kim, Seungryong and Xie, Saining and Liu, Sainan},
+  journal={arXiv preprint arXiv:2603.22275},
+  year={2026}
+}
+```
+
+## Acknowledgements
+
+Built upon [RAE](https://github.com/nicknign/RAE_release), [Depth Anything 3](https://github.com/DepthAnything/Depth-Anything-3), [VGGT](https://github.com/facebookresearch/vggt), [CUT3R](https://github.com/naver/CUT3R), and [SiT](https://github.com/willisma/SiT).