Update README.md

README.md

@@ -28,58 +28,47 @@ For more information please look at the [Project](https://www.research.autodesk.
 
 ### Model Sources
 
-<!-- Provide the basic links for the model. -->
-
 - **Repository:** [https://github.com/AutodeskAILab/Make-a-Shape](https://github.com/AutodeskAILab/Make-a-Shape)
-- **Paper:** [ArXiv:2401.11067](https://arxiv.org/abs/2401.11067)   [ICML - Make-A-Shape: a Ten-Million-scale 3D Shape Model](https://proceedings.mlr.press/v235/hui24a.html)
-- **Demo:** [in progress...]
+- **Paper:** [ArXiv:2401.11067](https://arxiv.org/abs/2401.11067),   [ICML - Make-A-Shape: a Ten-Million-scale 3D Shape Model](https://proceedings.mlr.press/v235/hui24a.html)
+- **Demo:** [Google Colab](https://colab.research.google.com/drive/1XIoeanLjXIDdLow6qxY7cAZ6YZpqY40d?usp=sharing)
 
-## Uses
+## Uses 
 
-### Direct Use
+### Direct Use 
 
-Please look at the instructions [here](https://github.com/AutodeskAILab/Make-a-Shape?tab=readme-ov-file#single-view-to-3d) to test this model for research and academic purposes.
+This model is released by Autodesk and intended for academic and research purposes only for the theoretical exploration and demonstration of the Make-a-Shape 3D generative framework.  Please see [here](https://github.com/AutodeskAILab/Make-a-Shape?tab=readme-ov-file#single-view-to-3d) for inferencing instructions. 
 
-### Downstream Use 
+### Out-of-Scope Use 
 
-This model could potentially be used in various applications such as:
-- 3D content creation for gaming and virtual environments
-- Augmented reality applications
-- Computer-aided design and prototyping
-- Architectural visualization
+The model should not be used for:
 
-### Out-of-Scope Use
+- Commercial purposes 
 
-The model should not be used for:
-- Commercial use
-- Generating 3D shapes of sensitive or copyrighted content without proper authorization
-- Creating 3D models intended for harmful or malicious purposes
+- Creation of load-bearing physical objects the failure of which could cause property damage or personal injury 
+
+- Any usage not in compliance with the [link to license], in particular, the "Acceptable Use" section. 
+
+## Bias, Risks, and Limitations 
+
+### Bias 
 
-## Bias, Risks, and Limitations
+- The model may inherit biases present in the publicly-available training datasets, which could lead to uneven representation of certain object types or styles. 
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+- The model's performance may degrade for object categories or styles that are underrepresented in the training data. 
 
-- The model may inherit biases present in the training dataset, which could lead to uneven representation of certain object types or styles.
-- The quality of the generated 3D shape depends on the quality and clarity of the input image.
-- The model may occasionally generate implausible shapes, especially when the input image is ambiguous or of low quality.
-- The model's performance may degrade for object categories or styles that are underrepresented in the training data.
+### Risks and Limitations 
 
-### Recommendations
+- The quality of the generated 3D output may be impacted by the quality and clarity of the input image. 
 
-Users should be aware of the potential biases and limitations of the model. It's recommended to:
-- Use high-quality, clear input images for best results
-- Verify and potentially post-process the generated 3D shapes for critical applications
-- Be cautious when using the model for object categories that may be underrepresented in the training data
-- Consider ethical implications and potential biases
-- DO NOT USE for commercial or public-facing applications
+- The model may occasionally generate implausible shapes, especially when the input image is ambiguous or of low quality.  Even theoretically plausible shapes should not be relied upon for real-world structural soundness.  
 
-## How to Get Started with the Model
+## How to Get Started with the Model 
 
-Please look at the instructions [here](https://github.com/AutodeskAILab/Make-a-Shape?tab=readme-ov-file#single-view-to-3d).
+Please refer to the instructions [here](https://github.com/AutodeskAILab/Make-a-Shape?tab=readme-ov-file#single-view-to-3d).
 
-## Training Details
+## Training Details 
 
-### Training Data
+### Training Data 
 
 The model was trained on a dataset of over 10 million 3D shapes aggregated from 18 different publicly-available sub-datasets, including ModelNet, ShapeNet, SMPL, Thingi10K, SMAL, COMA, House3D, ABC, Fusion 360, 3D-FUTURE, BuildingNet, DeformingThings4D, FG3D, Toys4K, ABO, Infinigen, Objaverse, and two subsets of ObjaverseXL (Thingiverse and GitHub).
 
@@ -91,7 +80,7 @@ Each 3D shape in the dataset was converted into a truncated signed distance func
 
 #### Training Hyperparameters
 
-- **Training regime:** Please look at the paper.
+- **Training regime:** Please refer to the paper.
 
 #### Speeds, Sizes, Times 
 
@@ -132,9 +121,9 @@ On the GSO dataset:
 
 ## Technical Specifications 
 
-### Model Architecture and Objective
+### Model Architecture and Objective 
 
-The model uses a U-ViT architecture with learnable skip-connections between the convolution and deconvolution blocks. It employs a wavelet-tree representation and a subband adaptive training strategy to effectively capture both coarse and fine details of 3D shapes.
+The model uses a U-ViT architecture with learnable skip-connections between the convolution and deconvolution blocks. It employs a wavelet-tree representation and a subband adaptive training strategy to effectively capture both coarse and fine details of 3D shapes. 
 
 ### Compute Infrastructure
 
@@ -145,27 +134,10 @@ The model was trained on 48 × A10G GPUs.
 ## Citation 
 
 **BibTeX:**
-@InProceedings{pmlr-v235-hui24a,
-  title = 	 {Make-A-Shape: a Ten-Million-scale 3{D} Shape Model},
-  author =       {Hui, Ka-Hei and Sanghi, Aditya and Rampini, Arianna and Rahimi Malekshan, Kamal and Liu, Zhengzhe and Shayani, Hooman and Fu, Chi-Wing},
-  booktitle = 	 {Proceedings of the 41st International Conference on Machine Learning},
-  pages = 	 {20660--20681},
-  year = 	 {2024},
-  editor = 	 {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix},
-  volume = 	 {235},
-  series = 	 {Proceedings of Machine Learning Research},
-  month = 	 {21--27 Jul},
-  publisher =    {PMLR},
-  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v235/main/assets/hui24a/hui24a.pdf},
-  url = 	 {https://proceedings.mlr.press/v235/hui24a.html},
-  abstract = 	 {The progression in large-scale 3D generative models has been impeded by significant resource requirements for training and challenges like inefficient representations. This paper introduces Make-A-Shape, a novel 3D generative model trained on a vast scale, using 10 million publicly-available shapes. We first innovate the wavelet-tree representation to encode high-resolution SDF shapes with minimal loss, leveraging our newly-proposed subband coefficient filtering scheme. We then design a subband coefficient packing scheme to facilitate diffusion-based generation and a subband adaptive training strategy for effective training on the large-scale dataset. Our generative framework is versatile, capable of conditioning on various input modalities such as images, point clouds, and voxels, enabling a variety of downstream applications, e.g., unconditional generation, completion, and conditional generation. Our approach clearly surpasses the existing baselines in delivering high-quality results and can efficiently generate shapes within two seconds for most conditions.}
+```latex
+@inproceedings{hui2024make,
+  title={Make-a-shape: a ten-million-scale 3d shape model},
+  author={Hui, Ka-Hei and Sanghi, Aditya and Rampini, Arianna and Malekshan, Kamal Rahimi and Liu, Zhengzhe and Shayani, Hooman and Fu, Chi-Wing},
+  booktitle={Forty-first International Conference on Machine Learning}
 }
-
-
-**APA:**
-
-Hui, K. H., Sanghi, A., Rampini, A., Malekshan, K. R., Liu, Z., Shayani, H., & Fu, C. W. (2024). Make-A-Shape: a Ten-Million-scale 3D Shape Model. arXiv preprint arXiv:2401.08504.
-
-## Model Card Contact
-
-[[email protected]]
+```