Improve model card metadata and content

README.md

@@ -1,29 +1,32 @@
 ---
+library_name: diffusers
 license: cc-by-nc-4.0
+pipeline_tag: other
 tags:
-  - 3d-scene-generation
-  - latent-diffusion
-  - autonomous-driving
-  - kitti-360
-  - primitives
-  - cvpr-2026
-library_name: pytorch
+- 3d-scene-generation
+- latent-diffusion
+- autonomous-driving
+- kitti-360
+- primitives
+- cvpr-2026
 ---
 
 # PrITTI: Primitive-based Generation of Controllable and Editable 3D Semantic Urban Scenes
 
 <p align="center">
-  <a href="https://arxiv.org/abs/2506.19117">📄 Paper</a> &nbsp;|&nbsp;
+  <a href="https://huggingface.co/papers/2506.19117">📄 Paper</a> &nbsp;|&nbsp;
   <a href="https://raniatze.github.io/pritti/">🌐 Project Page</a> &nbsp;|&nbsp;
   <a href="https://github.com/autonomousvision/pritti">💻 Code</a>
 </p>
 
 <p align="center">
-  <img src="teaser.png" alt="PrITTI teaser" width="95%">
+  <img src="https://huggingface.co/raniatze/pritti-checkpoints/resolve/main/teaser.png" alt="PrITTI teaser" width="95%">
 </p>
 
 This repository hosts the **pre-trained checkpoints** for **PrITTI** (CVPR 2026), a latent-diffusion framework for controllable and editable 3D semantic urban scene generation.
 
+Existing approaches to 3D semantic urban scene generation predominantly rely on voxel-based representations. In contrast, PrITTI advocates for a primitive-based paradigm where urban scenes are represented using compact, semantically meaningful 3D elements that are easy to manipulate and compose. PrITTI achieves state-of-the-art 3D scene generation quality with lower memory requirements and faster inference than voxel-based methods.
+
 ## Released Checkpoints
 
 The checkpoints below were trained on [KITTI-360](https://www.cvlibs.net/datasets/kitti-360/).
@@ -73,4 +76,4 @@ If you find PrITTI useful, please cite:
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
     year      = {2026},
 }
-```
+```