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README.md

@@ -22,19 +22,20 @@ WaLa-UN-1B is a large-scale 3D generative model trained on a massive dataset of
 - **Model type:** 3D Generative Model
 - **License:** Autodesk Non-Commercial (3D Generative) v1.0
 
-For more information please look at the [Project](TBD) [Page](TBD) and [the paper](TBD).
+For more information please look at the [Project Page](https://autodeskailab.github.io/WaLaProject) and [the paper](TBD).
 
 ### Model Sources
 
+- **Project Page:** [WaLa](https://autodeskailab.github.io/WaLaProject)
 - **Repository:** [Github](https://github.com/AutodeskAILab/WaLa)
 - **Paper:** [ArXiv:TBD](TBD)
-- **Demo:** [TBD](TBD)
+- **Demo:** [Colab](https://colab.research.google.com/drive/1W5zPXw9xWNpLTlU5rnq7g3jtIA2BX6aC?usp=sharing)
 
 ## Uses
 
 ### Direct Use
 
-This model is released by Autodesk and intended for academic and research purposes only for the theoretical exploration and demonstration of the WaLa 3D generative framework. Please see [here](TBD) for inferencing instructions. 
+This model is released by Autodesk and intended for academic and research purposes only for the theoretical exploration and demonstration of the WaLa 3D generative framework. Please see [here](https://github.com/AutodeskAILab/WaLa?tab=readme-ov-file#getting-started) for inferencing instructions. 
 
 ### Out-of-Scope Use
 
@@ -61,7 +62,7 @@ The model should not be used for:
 
 ## How to Get Started with the Model
 
-Please refer to the instructions [here](TBD)
+Please refer to the instructions [here](https://github.com/AutodeskAILab/WaLa?tab=readme-ov-file#getting-started)
 
 ## Training Details
 
@@ -84,28 +85,6 @@ Each 3D shape in the dataset was converted into a truncated signed distance func
 - The model contains approximately 1.1 billion parameters.
 - The model can generate shapes within 2-4 seconds.
 
-## Evaluation
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-The model was evaluated on the Google Scanned Objects (GSO) dataset and a validation set from the training data (MAS validation data).
-
-#### Factors
-
-The evaluation considered various factors such as the quality of generated shapes, the ability to capture fine details and complex structures, and the model's performance across different object categories.
-
-#### Metrics
-
-The model was evaluated using the following metrics:
-- Intersection over Union (IoU)
-- Light Field Distance (LFD)
-- Chamfer Distance (CD)
-
-### Results
-
-[Specific results for the unconditional model to be added when available]
 
 ## Technical Specifications
 
@@ -117,7 +96,7 @@ The model uses a U-ViT architecture with modifications. It employs a wavelet-bas
 
 #### Hardware
 
-[TBD]
+The model was trained on NVIDIA H100 GPUs.
 
 ## Citation