---
'[object Object]': null
language:
- en
license: other
license_name: autodesk-non-commercial-3d-generative-v1.0
license_link: LICENSE.md
tags:
- make-a-shape
- sv-to-3d
pipeline_tag: image-to-3d
---
---
# Model Card for Make-A-Shape Single-View to 3D Model

This model is part of the Make-A-Shape paper, capable of generating high-quality 3D shapes from single-view images with intricate geometric details, realistic structures, and complex topologies.

## Model Details

### Model Description

Make-A-Shape is a novel 3D generative framework trained on an extensive dataset of over 10 million publicly-available 3D shapes. The single-view to 3D model is one of the conditional generation models in this framework. It can efficiently generate a wide range of high-quality 3D shapes from single-view image inputs in just 2 seconds. The model uses a wavelet-tree representation and adaptive training strategy to achieve superior performance in terms of geometric detail and structural plausibility.

- **Developed by:** Ka-Hei Hui, Aditya Sanghi, Arianna Rampini, Kamal Rahimi Malekshan, Zhengzhe Liu, Hooman Shayani, Chi-Wing Fu
- **Model type:** 3D Generative Model
- **License:** Autodesk Non-Commercial (3D Generative) v1.0

For more information please look at the [Project](https://www.research.autodesk.com/publications/generative-ai-make-a-shape/) [Page](https://edward1997104.github.io/make-a-shape/) and [the ICML paper](https://proceedings.mlr.press/v235/hui24a.html).

### Model Sources

- **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:** [Google Colab](https://colab.research.google.com/drive/1XIoeanLjXIDdLow6qxY7cAZ6YZpqY40d?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 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. 

### Out-of-Scope Use 

The model should not be used for:

- Commercial 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 [license](https://huggingface.co/ADSKAILab/Make-A-Shape-single-view-20m/blob/main/LICENSE.md), in particular, the "Acceptable Use" section. 

## Bias, Risks, and Limitations 

### Bias 

- The model may inherit biases present in the publicly-available training datasets, which could lead to uneven representation of certain object types or styles. 

- The model's performance may degrade for object categories or styles that are underrepresented in the training data. 

### Risks and Limitations 

- The quality of the generated 3D output may be impacted by 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.  Even theoretically plausible shapes should not be relied upon for real-world structural soundness.  

## How to Get Started with the Model 

Please refer to the instructions [here](https://github.com/AutodeskAILab/Make-a-Shape?tab=readme-ov-file#single-view-to-3d).

## Training Details 

### 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).

### Training Procedure

#### Preprocessing 

Each 3D shape in the dataset was converted into a truncated signed distance function (TSDF) with a resolution of 256³. The TSDF was then decomposed using a discrete wavelet transform to create the wavelet-tree representation used by the model.

#### Training Hyperparameters

- **Training regime:** Please refer to the paper.

#### Speeds, Sizes, Times 

- The model was trained on 48 × A10G GPUs for about 20 days, amounting to around 23,000 GPU hours.
- The model can generate shapes within two seconds for most conditions.

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

The model was evaluated on a test set consisting of 2% of the shapes from each sub-dataset in the training data, as well as on the entire Google Scanned Objects (GSO) dataset, which was not part of the training 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

The single-view to 3D model achieved the following results on the "Our Val" dataset:
- LFD: 4071.33
- IoU: 0.4285
- CD: 0.01851

On the GSO dataset:
- LFD: 3406.61
- IoU: 0.5004
- CD: 0.01748


## Technical Specifications 

### 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. 

### Compute Infrastructure

#### Hardware

The model was trained on 48 × A10G GPUs.

## Citation 

**BibTeX:**
```latex
@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},
}
```