|
--- |
|
'[object Object]': null |
|
language: |
|
- en |
|
license: other |
|
license_name: autodesk-non-commercial-3d-generative-v1.0 |
|
license_link: LICENSE.md |
|
tags: |
|
- make-a-shape |
|
- mv-to-3d |
|
--- |
|
--- |
|
# Model Card for Make-A-Shape Multi-View to 3D Model |
|
|
|
This model is part of the Make-A-Shape paper, capable of generating high-quality 3D shapes from multi-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 multi-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 four view-specific images as inputs. 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#multi-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-multi-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#multi-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 multi-view to 3D model achieved the following results on the "Our Val" dataset: |
|
- LFD: 2217.25 |
|
- IoU: 0.6707 |
|
- CD: 0.00350 |
|
|
|
On the GSO dataset: |
|
- LFD: 1890.85 |
|
- IoU: 0.7460 |
|
- CD: 0.00337 |
|
|
|
|
|
## 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}, |
|
} |
|
``` |
|
|
