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--- |
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'[object Object]': null |
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language: |
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- en |
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license: other |
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license_name: autodesk-non-commercial-3d-generative-v1.0 |
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license_link: LICENSE.md |
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tags: |
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- make-a-shape |
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- mv-to-3d |
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pipeline_tag: image-to-3d |
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--- |
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--- |
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# Model Card for Make-A-Shape Multi-View to 3D Model |
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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. |
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## Model Details |
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### Model Description |
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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. |
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- **Developed by:** Ka-Hei Hui, Aditya Sanghi, Arianna Rampini, Kamal Rahimi Malekshan, Zhengzhe Liu, Hooman Shayani, Chi-Wing Fu |
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- **Model type:** 3D Generative Model |
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- **License:** Autodesk Non-Commercial (3D Generative) v1.0 |
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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). |
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### Model Sources |
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- **Repository:** [https://github.com/AutodeskAILab/Make-a-Shape](https://github.com/AutodeskAILab/Make-a-Shape) |
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- **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) |
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- **Demo:** [Google Colab](https://colab.research.google.com/drive/1XIoeanLjXIDdLow6qxY7cAZ6YZpqY40d?usp=sharing) |
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## Uses |
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### Direct Use |
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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. |
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### Out-of-Scope Use |
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The model should not be used for: |
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- Commercial purposes |
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- Creation of load-bearing physical objects the failure of which could cause property damage or personal injury |
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- 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. |
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## Bias, Risks, and Limitations |
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### Bias |
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- The model may inherit biases present in the publicly-available training datasets, which could lead to uneven representation of certain object types or styles. |
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- The model's performance may degrade for object categories or styles that are underrepresented in the training data. |
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### Risks and Limitations |
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- The quality of the generated 3D output may be impacted by the quality and clarity of the input image. |
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- 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. |
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## How to Get Started with the Model |
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Please refer to the instructions [here](https://github.com/AutodeskAILab/Make-a-Shape?tab=readme-ov-file#multi-view-to-3d). |
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## Training Details |
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### Training Data |
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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). |
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### Training Procedure |
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#### Preprocessing |
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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. |
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#### Training Hyperparameters |
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- **Training regime:** Please refer to the paper. |
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#### Speeds, Sizes, Times |
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- The model was trained on 48 × A10G GPUs for about 20 days, amounting to around 23,000 GPU hours. |
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- The model can generate shapes within two seconds for most conditions. |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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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. |
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#### Factors |
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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. |
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#### Metrics |
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The model was evaluated using the following metrics: |
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- Intersection over Union (IoU) |
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- Light Field Distance (LFD) |
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- Chamfer Distance (CD) |
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### Results |
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The multi-view to 3D model achieved the following results on the "Our Val" dataset: |
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- LFD: 2217.25 |
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- IoU: 0.6707 |
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- CD: 0.00350 |
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On the GSO dataset: |
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- LFD: 1890.85 |
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- IoU: 0.7460 |
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- CD: 0.00337 |
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## Technical Specifications |
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### Model Architecture and Objective |
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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. |
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### Compute Infrastructure |
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#### Hardware |
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The model was trained on 48 × A10G GPUs. |
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## Citation |
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**BibTeX:** |
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```latex |
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@InProceedings{pmlr-v235-hui24a, |
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title = {Make-A-Shape: a Ten-Million-scale 3{D} Shape Model}, |
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author = {Hui, Ka-Hei and Sanghi, Aditya and Rampini, Arianna and Rahimi Malekshan, Kamal and Liu, Zhengzhe and Shayani, Hooman and Fu, Chi-Wing}, |
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booktitle = {Proceedings of the 41st International Conference on Machine Learning}, |
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pages = {20660--20681}, |
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year = {2024}, |
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editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, |
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volume = {235}, |
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series = {Proceedings of Machine Learning Research}, |
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month = {21--27 Jul}, |
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publisher = {PMLR}, |
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pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/hui24a/hui24a.pdf}, |
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url = {https://proceedings.mlr.press/v235/hui24a.html}, |
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} |
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``` |
