Add paper link, authors and sample usage

README.md

@@ -12,13 +12,16 @@ inference: false
 # ReLi3D
 ![](reli3d_teaser.jpg)
 
-**ReLi3D** is a multi-view image-to-3D reconstruction model that takes object images and camera poses and generates a textured, UV-unwrapped, relightable 3D mesh asset.
+**ReLi3D** is a multi-view image-to-3D reconstruction model that takes object images and camera poses and generates a textured, UV-unwrapped, relightable 3D mesh asset. 
+
+It is the official model for the paper [ReLi3D: Relightable Multi-view 3D Reconstruction with Disentangled Illumination](https://huggingface.co/papers/2603.19753).
 
 Please note: For individuals or organizations generating annual revenue of US $1,000,000 (or local currency equivalent) or more, regardless of the source of that revenue, you must obtain an enterprise commercial license directly from Stability AI before commercially using ReLi3D, or any derivative work of ReLi3D or its outputs, such as fine-tuned models. You may submit a request for an Enterprise License at https://stability.ai/enterprise. Please refer to Stability AI's Community License, available at https://stability.ai/license, for more information.
 
 ### Model Description
 
 * **Developed by**: [Stability AI](https://stability.ai/)
+* **Authors**: Jan-Niklas Dihlmann, Mark Boss, Simon Donne, Andreas Engelhardt, Hendrik P. A. Lensch, Varun Jampani.
 * **Model type**: Transformer multi-view image-to-3D model
 * **Model details**: ReLi3D is trained to reconstruct a relightable 3D mesh from multiple 512x512 object images with known camera poses. The model outputs UV-unwrapped geometry and texture, and predicts material properties such as roughness and metallic values, together with an estimated illumination representation for downstream rendering workflows.
 
@@ -30,7 +33,7 @@ Please note: For individuals or organizations generating annual revenue of US $1
 
 * **Repository**: https://github.com/Stability-AI/ReLi3D
 * **Project page**: https://reli3d.jdihlmann.com/
-* **arXiv page**: Coming soon
+* **arXiv page**: https://arxiv.org/abs/2603.19753
 
 ### Files
 
@@ -43,7 +46,21 @@ The training process uses renders from Objaverse and curated subsets of addition
 
 ## Usage
 
-For usage instructions, please refer to the [ReLi3D GitHub repository](https://github.com/Stability-AI/ReLi3D).
+For installation and full usage instructions, please refer to the [ReLi3D GitHub repository](https://github.com/Stability-AI/ReLi3D).
+
+### Quickstart
+
+To run inference on a set of images and camera poses, you can use the following command from the repository:
+
+```bash
+python demos/reli3d/infer_from_transforms.py \
+  --input-root demo_files/objects \
+  --objects Camera_01 \
+  --output-root outputs \
+  --num-views 4 \
+  --texture-size 256 \
+  --overwrite
+```
 
 ### Intended Uses
 
@@ -73,3 +90,14 @@ Please report any issues with the model or contact us:
 * Privacy issues: privacy@stability.ai
 * License and general: https://stability.ai/license
 * Enterprise license: https://stability.ai/enterprise
+
+## Citation
+
+```bibtex
+@inproceeding{ dihlmann2026reli3d,
+author = {Dihlmann, Jan-Niklas and Boss, Mark and Donne, Simon and Engelhardt, Andreas and Lensch, Hendrik P. A. and Jampani, Varun},
+title = {ReLi3D: Relightable Multi-view 3D Reconstruction with Disentangled Illumination},
+booktitle = {International Conference on Learning Representations (ICLR)},
+year ={2026}
+}
+```