--- language: - en license: other license_name: autodesk-non-commercial-3d-generative-v1.0 tags: - wala - depth-map-to-3d --- # Model Card for WaLa-DM1-1B This model is part of the Wavelet Latent Diffusion (WaLa) paper, capable of generating high-quality 3D shapes from single-view depth map input with detailed geometry and complex structures. ## Model Details ### Model Description WaLa-DM1-1B 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 depth map input in just 4 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. - **Developed by:** Aditya Sanghi, Aliasghar Khani, Chinthala Pradyumna Reddy, Arianna Rampini, Derek Cheung, Kamal Rahimi Malekshan, Kanika Madan, Hooman Shayani - **Model type:** 3D Generative Model - **License:** Autodesk Non-Commercial (3D Generative) v1.0 For more information please look at the [Project Page](https://autodeskailab.github.io/WaLaProject) and [the paper](TBD). ### Model Sources - **Project Page:** [WaLa](https://autodeskailab.github.io/WaLaProject) - **Repository:** [Github](https://github.com/AutodeskAILab/WaLa) - **Paper:** [ArXiv:TBD](TBD) - **Demo:** [Colab](https://colab.research.google.com/drive/1W5zPXw9xWNpLTlU5rnq7g3jtIA2BX6aC?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 WaLa 3D generative framework. Please see [here](https://github.com/AutodeskAILab/WaLa?tab=readme-ov-file#depth-map-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/WaLa-DM1-1B/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 accuracy of the input depth maps. - The model may occasionally generate implausible shapes, especially when the input depth maps are 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/WaLa?tab=readme-ov-file#getting-started) ## Training Details ### Training Data 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). ### 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. For depth map conditioning, any single view can be selected. #### Training Hyperparameters - **Training regime:** Please refer to the paper. #### Speeds, Sizes, Times - The model contains approximately 956 million parameters. - The model can generate shapes within 4 seconds. ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data The model was evaluated on the Google Scanned Objects (GSO) dataset and a validation set from the training data (MAS validation 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 depth to 3D model achieved the following results on the GSO dataset: - LFD: TBD - IoU: 0.6927 - CD: 0.01301 On the MAS validation dataset: - LFD: TBD - IoU: 0.6358 - CD: 0.01213 ## Technical Specifications ### Model Architecture and Objective The 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 a single-view depth input. Each selected depth map is processed individually through the DINO v2 encoder, generating a sequence of latent vectors for each view. The latent vectors from all views are concatenated to form the final conditional latent vectors. ### Compute Infrastructure #### Hardware The model was trained on NVIDIA H100 GPUs. ## Citation [Citation information to be added after paper publication]