Update README.md

README.md

@@ -8,14 +8,14 @@ tags:
 - relighting
 ---
 
-# CroCoDiLight: Repurposing Cross-View Completion Encoders for Relighting
+# [ICLR 2026] CroCoDiLight: Repurposing Cross-View Completion Encoders for Relighting
 Disentangles illumination from scene content in CroCo (Cross-view Completion) latent representations. A learned lighting extractor separates each encoder embedding into a single lighting vector and lighting-invariant patch features, which can then be recombined with target lighting conditions. This enables shadow removal, albedo estimation, lighting transfer, and interpolation, trained on datasets two orders of magnitude smaller than the original CroCo pretraining.
 
-**Paper:** [OpenReview](https://openreview.net/forum?id=GKvb3HCyNk) (ICLR 2026)
-**Code:** [GitHub](https://github.com/alistairfoggin/CroCoDiLight)
+**Paper:** [OpenReview](https://openreview.net/forum?id=GKvb3HCyNk) (ICLR 2026)  
+**Code:** [GitHub](https://github.com/alistairfoggin/CroCoDiLight)  
+**Project Page:** [alistairfoggin.com/projects/crocodilight](https://alistairfoggin.com/projects/crocodilight)
 
 ## Pretrained Model Weights
-
 | File | Required for | Description |
 |---|---|---|
 | **Inference** | | |
@@ -25,38 +25,32 @@ Disentangles illumination from scene content in CroCo (Cross-view Completion) la
 | **Training** | | |
 | [`CroCoDiLight_decoder.pth`](https://huggingface.co/alistairfoggin/CroCoDiLight/resolve/main/CroCoDiLight_decoder.pth?download=true) | Training of `CroCoDiLight.pth` | The pretrained monocular decoder for the CroCo v2 encoder |
 
-`CroCoDiLight.pth` is the base model needed by every inference and evaluation script. The mapper weights are only needed for their respective tasks. Lighting transfer, freezing, and interpolation use the base model only.
-
-`CroCoDiLight_decoder.pth` is not necessary for inference as it is embedded into `CroCoDiLight.pth`, but can be used as a standalone decoder for the CroCo v2 ViTLarge encoder (which is embedded in the model weights too).
+`CroCoDiLight.pth` is the base model needed by every inference and evaluation script. The mapper weights are only needed for their respective tasks. Lighting transfer, freezing, and interpolation use the base model only. `CroCoDiLight_decoder.pth` is not necessary for inference as it is embedded into `CroCoDiLight.pth`, but can be used as a standalone decoder for the CroCo v2 ViTLarge encoder (which is embedded in the model weights too).
 
 ## Usage
-
 See the [GitHub repository](https://github.com/alistairfoggin/CroCoDiLight) for setup instructions, inference scripts, Gradio demos, training, and evaluation.
 
-## License
+## Citation BibTeX
+```bibtex
+@inproceedings{foggin2026crocodilight,
+  title={{CroCoDiLight}: Repurposing Cross-View Completion Encoders for Relighting},
+  author={Foggin, Alistair J and Smith, William A P},
+  booktitle={The Fourteenth International Conference on Learning Representations},
+  year={2026},
+  url={https://openreview.net/forum?id=GKvb3HCyNk}
+}
+```
 
+## License
 This project, including its source code and pretrained model weights, is
 licensed under [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/). The
 pretrained weights are additionally subject to the license terms of the upstream training
 data documented in the [NOTICE](https://github.com/alistairfoggin/CroCoDiLight/blob/main/NOTICE) file.
 
 ## Acknowledgements
-
 CroCoDiLight builds on [CroCo](https://github.com/naver/croco) (Weinzaepfel et al.),
-licensed under CC BY-NC-SA 4.0 by Naver Corporation.
-
+licensed under CC BY-NC-SA 4.0 by Naver Corporation.  
 Model training was performed on the Viking cluster, a high performance compute facility
 provided by the University of York. We are grateful for computational support from the
 University of York, IT Services and the Research IT team.
 
-## Citation BibTeX
-
-```bibtex
-@inproceedings{foggin2026crocodilight,
-  title={{CroCoDiLight}: Repurposing Cross-View Completion Encoders for Relighting},
-  author={Foggin, Alistair J and Smith, William A P},
-  booktitle={The Fourteenth International Conference on Learning Representations},
-  year={2026},
-  url={https://openreview.net/forum?id=GKvb3HCyNk}
-}
-```