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make-a-shape
vx16-to-3d
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@@ -18,7 +18,7 @@ This model is part of the Make-A-Shape paper, capable of generating high-quality
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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 voxels(16³) 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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  ### 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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  ### 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 voxels(16³) 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 16^3 voxels 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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  ### 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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