--- '[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 --- --- # 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}, } ```