3D-WAG: Hierarchical Wavelet-Guided Autoregressive Generation for High-Fidelity 3D Shapes
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909985145880576 |
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| author | Medi, Tejaswini Rampini, Arianna Reddy, Pradyumna Jayaraman, Pradeep Kumar Keuper, Margret |
| author_facet | Medi, Tejaswini Rampini, Arianna Reddy, Pradyumna Jayaraman, Pradeep Kumar Keuper, Margret |
| contents | Autoregressive (AR) models have achieved remarkable success in natural language and image generation, but their application to 3D shape modeling remains largely unexplored. Unlike diffusion models, AR models enable more efficient and controllable generation with faster inference times, making them especially suitable for data-intensive domains. Traditional 3D generative models using AR approaches often rely on ``next-token" predictions at the voxel or point level. While effective for certain applications, these methods can be restrictive and computationally expensive when dealing with large-scale 3D data. To tackle these challenges, we introduce 3D-WAG, an AR model for 3D implicit distance fields that can perform unconditional shape generation, class-conditioned and also text-conditioned shape generation. Our key idea is to encode shapes as multi-scale wavelet token maps and use a Transformer to predict the ``next higher-resolution token map" in an autoregressive manner. By redefining 3D AR generation task as ``next-scale" prediction, we reduce the computational cost of generation compared to traditional ``next-token" prediction models, while preserving essential geometric details of 3D shapes in a more structured and hierarchical manner. We evaluate 3D-WAG to showcase its benefit by quantitative and qualitative comparisons with state-of-the-art methods on widely used benchmarks. Our results show 3D-WAG achieves superior performance in key metrics like Coverage and MMD, generating high-fidelity 3D shapes that closely match the real data distribution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_19037 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 3D-WAG: Hierarchical Wavelet-Guided Autoregressive Generation for High-Fidelity 3D Shapes Medi, Tejaswini Rampini, Arianna Reddy, Pradyumna Jayaraman, Pradeep Kumar Keuper, Margret Computer Vision and Pattern Recognition Autoregressive (AR) models have achieved remarkable success in natural language and image generation, but their application to 3D shape modeling remains largely unexplored. Unlike diffusion models, AR models enable more efficient and controllable generation with faster inference times, making them especially suitable for data-intensive domains. Traditional 3D generative models using AR approaches often rely on ``next-token" predictions at the voxel or point level. While effective for certain applications, these methods can be restrictive and computationally expensive when dealing with large-scale 3D data. To tackle these challenges, we introduce 3D-WAG, an AR model for 3D implicit distance fields that can perform unconditional shape generation, class-conditioned and also text-conditioned shape generation. Our key idea is to encode shapes as multi-scale wavelet token maps and use a Transformer to predict the ``next higher-resolution token map" in an autoregressive manner. By redefining 3D AR generation task as ``next-scale" prediction, we reduce the computational cost of generation compared to traditional ``next-token" prediction models, while preserving essential geometric details of 3D shapes in a more structured and hierarchical manner. We evaluate 3D-WAG to showcase its benefit by quantitative and qualitative comparisons with state-of-the-art methods on widely used benchmarks. Our results show 3D-WAG achieves superior performance in key metrics like Coverage and MMD, generating high-fidelity 3D shapes that closely match the real data distribution. |
| title | 3D-WAG: Hierarchical Wavelet-Guided Autoregressive Generation for High-Fidelity 3D Shapes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2411.19037 |