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- # PartPacker
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  ## Description:
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- In this work, we propose a new end-to-end framework for part-level 3D object generation.
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- Given a single input image, our method generates high-quality 3D shapes with an arbitrary number of complete and semantically meaningful parts. Most existing methods generate a single mesh with all parts fused together, which limits the ability to edit or manipulate individual parts. A key challenge in part-level generation is that different objects may have a varying number of components. To address this problem, we introduce a dual volume packing strategy that organizes all parts into two complementary volumes, allowing for the creation of complete and interleaved parts that assemble into the final object. Experiments show that our model achieves better quality, diversity, and generalization than previous image-based part-level generation methods.
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  This model is ready for non-commercial use.
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  ## License/Terms of Use:
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  **Architecture Type:** Transformer
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  ## Input:
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- **Input Type(s):** Image
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- **Input Format(s):** RGB Image
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- **Input Parameters:** 2D Image
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- **Other Properties Related to Input:** Condition for the model.
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  ## Output:
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- **Output Type(s):** Mesh
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- **Output Format:** GLB
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- **Output Parameters:** 3D Mesh
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- **Other Properties Related to Output:** Generated 3D shape with parts.
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  ## Supported Hardware Microarchitecture Compatibility
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  * NVIDIA Ampere
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  v1.0
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  ## Training Dataset:
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- [Objaverse-XL](https://objaverse.allenai.org/)
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- **Properties:** We use about 250k mesh data, which is a subset from the Objaverse-XL with part-level annotations.
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- **Dataset License(s):** The use of the dataset as a whole is licensed under the ODC-By v1.0 license.
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  ## Inference:
 
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+ # Model Card for PartPacker
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  ## Description:
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+ PartPacker takes a single input image and generates a 3D shape with an arbitrary number of complete parts.
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+ We introduce a dual volume packing strategy that organizes all parts into two complementary volumes, allowing for the creation of complete and interleaved parts that assemble into the final object.
 
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  This model is ready for non-commercial use.
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  ## License/Terms of Use:
 
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  **Architecture Type:** Transformer
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  ## Input:
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+ **Input Type(s):** Image
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+ **Input Format(s):** RGB Image
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+ **Input Parameters:** 2D Image
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+ **Other Properties Related to Input:** Condition for the model.
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  ## Output:
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+ **Output Type(s):** Mesh
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+ **Output Format:** GLB
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+ **Output Parameters:** 3D Mesh
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+ **Other Properties Related to Output:** Generated 3D shape with parts.
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  ## Supported Hardware Microarchitecture Compatibility
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  * NVIDIA Ampere
 
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  v1.0
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  ## Training Dataset:
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+ [Objaverse-XL](https://objaverse.allenai.org/)
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+ **Properties:** We use about 250k mesh data, which is a subset from the Objaverse-XL with part-level annotations.
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+ **Dataset License(s):** The use of the dataset as a whole is licensed under the ODC-By v1.0 license.
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  ## Inference: