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* **Developed by**: [Stability AI](https://stability.ai/)
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* **Model type**: Transformer image-to-3D model
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* **Model details**: This model is trained to create a 3D model from a single image in under one second. The asset is UV-unwrapped and textured. We also perform a delighting step, enabling easier asset usage in downstream applications such as game engines or rendering work. The model also predicts per-object material parameters (roughness, metallic), enhancing reflective behaviors during rendering. The model expects an input size of 512x512 pixels. We achieve improved backside modelling using a fast point diffusion model, which acts as a conditioning.
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Please check our [tech report]() and [video summary](https://youtu.be/mlO3Nc3Nsng) for details.
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### License
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### Model Sources
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* **Repository**: [https://github.com/Stability-AI/stable-point-aware-3d](https://github.com/Stability-AI/stable-point-aware-3d)
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* **Tech report**:
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* **Video summary**: [https://youtu.be/mlO3Nc3Nsng](https://youtu.be/mlO3Nc3Nsng)
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* **Project page**: [https://spar3d.github.io](https://spar3d.github.io)
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* **arXiv page**:
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### Training Dataset
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* **Developed by**: [Stability AI](https://stability.ai/)
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* **Model type**: Transformer image-to-3D model
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* **Model details**: This model is trained to create a 3D model from a single image in under one second. The asset is UV-unwrapped and textured. We also perform a delighting step, enabling easier asset usage in downstream applications such as game engines or rendering work. The model also predicts per-object material parameters (roughness, metallic), enhancing reflective behaviors during rendering. The model expects an input size of 512x512 pixels. We achieve improved backside modelling using a fast point diffusion model, which acts as a conditioning.
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Please check our [tech report](arxiv.org/abs/2501.04689) and [video summary](https://youtu.be/mlO3Nc3Nsng) for details.
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### License
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### Model Sources
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* **Repository**: [https://github.com/Stability-AI/stable-point-aware-3d](https://github.com/Stability-AI/stable-point-aware-3d)
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* **Tech report**: [https://arxiv.org/pdf/2501.04689](https://arxiv.org/pdf/2501.04689)
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* **Video summary**: [https://youtu.be/mlO3Nc3Nsng](https://youtu.be/mlO3Nc3Nsng)
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* **Project page**: [https://spar3d.github.io](https://spar3d.github.io)
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* **arXiv page**: [https://arxiv.org/abs/2501.04689](https://arxiv.org/abs/2501.04689)
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### Training Dataset
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