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--- |
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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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tags: |
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- wala |
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- single-view-to-3d |
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--- |
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# Model Card for WaLa-SV-1B |
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This model is part of the Wavelet Latent Diffusion (WaLa) paper, capable of generating high-quality 3D shapes from single-view images with detailed geometry and complex structures. |
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## Model Details |
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### Model Description |
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WaLa-SV-1B is a large-scale 3D generative model trained on a massive dataset of over 10 million publicly-available 3D shapes. It can efficiently generate a wide range of high-quality 3D shapes from single-view image inputs in just 2.5 seconds. The model uses a wavelet-based compact latent encoding and a billion-parameter architecture to achieve superior performance in terms of geometric detail and structural plausibility. |
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- **Developed by:** Aditya Sanghi, Aliasghar Khani, Chinthala Pradyumna Reddy, Arianna Rampini, Derek Cheung, Kamal Rahimi Malekshan, Kanika Madan, Hooman Shayani |
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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](TBD) [Page](TBD) and [the paper](TBD). |
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### Model Sources |
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- **Repository:** [Github](https://github.com/AutodeskAILab/WaLa) |
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- **Paper:** [ArXiv:TBD](TBD) |
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- **Demo:** [TBD](TBD) |
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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 WaLa 3D generative framework. Please see [here](TBD) 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/WaLa-single-view-1B/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. |
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- The model may occasionally generate implausible shapes, especially when the input 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](TBD) |
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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 19 different publicly-available sub-datasets, including ModelNet, ShapeNet, SMLP, 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 contains approximately 956 million parameters. |
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- The model can generate shapes within 2-4 seconds. |
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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 the Google Scanned Objects (GSO) dataset and a validation set from the training data (MAS validation 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 single-view to 3D model achieved the following results on the GSO dataset: |
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- LFD: 2509.20 |
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- IoU: 0.6154 |
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- CD: 0.02150 |
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On the MAS validation dataset: |
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- LFD: 2920.74 |
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- IoU: 0.6056 |
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- CD: 0.01530 |
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## Technical Specifications |
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### Model Architecture and Objective |
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TThe model uses a U-ViT architecture with modifications. It employs a wavelet-based compact latent encoding to effectively capture both coarse and fine details of 3D shapes from single-view inputs. The input view is processed through the DINO v2 encoder to extract feature representations, which then serve as the condition latent vectors for the generative model. |
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### Compute Infrastructure |
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#### Hardware |
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[TBD] |
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## Citation |
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[Citation information to be added after paper publication] |
