added viz

README.md

@@ -1,16 +1,28 @@
+
+![Method](https://raw.githubusercontent.com/IIT-PAVIS/DiffAssemble/release/page/DiffAssemble.png)
+
 Reassembly tasks play a fundamental role in many fields and multiple approaches exist to solve specific reassembly problems. In this context, we posit that a general unified model can effectively address them all, irrespective of the input data type (images, 3D, etc.). We introduce DiffAssemble, a Graph Neural Network (GNN)-based architecture that learns to solve reassembly tasks using a diffusion model formulation. Our method treats the elements of a set, whether pieces of 2D patch or 3D object fragments, as nodes of a spatial graph. Training is performed by introducing noise into the position and rotation of the elements and iteratively denoising them to reconstruct the coherent initial pose. DiffAssemble achieves state-of-the-art (SOTA) results in most 2D and 3D reassembly tasks and is the first learning-based approach that solves 2D puzzles for both rotation and translation. Furthermore, we highlight its remarkable reduction in run-time, performing 11 times faster than the quickest optimization-based method for puzzle solving. We will release the code upon paper acceptance.
 - [Webpage](https://iit-pavis.github.io/DiffAssemble/)
 - [Code](https://github.com/IIT-PAVIS/DiffAssemble)
-- [Arxiv](soon)
+- [Arxiv](https://arxiv.org/abs/2402.19302")
 
 # Method
-![Method](https://raw.githubusercontent.com/IIT-PAVIS/DiffAssemble/release/page/DiffAssemble.png)
+![Architecture](https://raw.githubusercontent.com/IIT-PAVIS/DiffAssemble/release/page/architecture.jpg)
 Following the Diffusion Probabilistic
 Models formulations, we model a Markov chain where we inject noise into the pieces’ position and orientation. At timestep t = 0, the
 pieces are in their correct position, and at timestep t = T , they are in a random position with random orientation. At each timestep t, our
 attention-based GNN takes as input a graph where each node contains an equivariant feature that describes a particular piece and its position
 and orientation. The network then predicts a less noisy version of the piece’s position and orientation.
 
+# Visuals
+
+## 2D
+![2d](https://raw.githubusercontent.com/IIT-PAVIS/DiffAssemble/release/page/viz.gif)
+
+## 3D
+
+![3d](https://raw.githubusercontent.com/IIT-PAVIS/DiffAssemble/release/page/bottle_new2_0001-0350.gif)
+
 # Datasets
 
 ## Puzzles
@@ -46,12 +58,11 @@ and orientation. The network then predicts a less noisy version of the piece’s
 
 # Cite us!
 ```
-@misc{giuliari2023positional,
- title={Positional Diffusion: Ordering Unordered Sets with Diffusion Probabilistic Models},
- author={Francesco Giuliari and Gianluca Scarpellini and Stuart James and Yiming Wang and Alessio {Del Bue}},
- year={2023},
- eprint={2303.11120},
- archivePrefix={arXiv},
- primaryClass={cs.CV}
+@InProceedings{scarpellini2024diffassemble,
+ author = {Gianluca Scarpellini and Stefano Fiorini and Francesco Giuliari and Pietro Morerio and Alessio Del Bue},
+ title = {DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly},
+ booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+ month = {June},
+ year = {2024},
 }
 ```

_config.yaml

@@ -1,5 +1,5 @@
 remote_theme: pages-themes/cayman@v0.2.0
 plugins:
 - jekyll-remote-theme
-title: DiffAssemble
-description: Official repository for DiffAssemble-A Unified Graph-Diffusion Model for 2D and 3D Reassembly accepted at CVPR2024
+title: DiffAssemble-A Unified Graph-Diffusion Model for 2D and 3D Reassembly
+description: Accepted at CVPR2024