updated readm

README.md

@@ -3,7 +3,13 @@ Reassembly tasks play a fundamental role in many fields and multiple approaches
 - [Code](https://github.com/IIT-PAVIS/DiffAssemble)
 - [Arxiv](soon)
 
+# Method
 ![Method](https://raw.githubusercontent.com/IIT-PAVIS/DiffAssemble/release/page/DiffAssemble.png)
+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.
 
 # Datasets
 

_config.yaml

@@ -1,3 +1,5 @@
 remote_theme: pages-themes/[email protected]
 plugins:
 - jekyll-remote-theme
+title: DiffAssemble
+description: Official repository for "DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassembly" accepted at CVPR2024