Zero-1-to-3 is a method that synthesizes novel views of an object from a single RGB image, enabling zero-shot 3D object reconstruction. The approach leverages large-scale diffusion models, such as Stable Diffusion, which are pre-trained on internet-scale text-image data. By fine-tuning these models on synthetic data, the system learns to control camera viewpoints, allowing it to generate images from different perspectives. This method achieves strong zero-shot performance on objects with complex geometry and artistic styles, outperforming existing models in both novel view synthesis and 3D reconstruction tasks. The system uses a view-conditioned diffusion architecture, combining image and pose information to generate realistic images. It also enables 3D reconstruction by optimizing a neural field with priors from diffusion models. The method is evaluated on various datasets, including Google Scanned Objects and RTMV, demonstrating its effectiveness in generating high-fidelity images and reconstructing 3D shapes. The approach is robust to different surface materials and geometries, and can handle in-the-wild images and text-to-image generated images. The results show that Zero-1-to-3 significantly outperforms state-of-the-art methods in both tasks, achieving high-quality outputs with strong generalization capabilities.Zero-1-to-3 is a method that synthesizes novel views of an object from a single RGB image, enabling zero-shot 3D object reconstruction. The approach leverages large-scale diffusion models, such as Stable Diffusion, which are pre-trained on internet-scale text-image data. By fine-tuning these models on synthetic data, the system learns to control camera viewpoints, allowing it to generate images from different perspectives. This method achieves strong zero-shot performance on objects with complex geometry and artistic styles, outperforming existing models in both novel view synthesis and 3D reconstruction tasks. The system uses a view-conditioned diffusion architecture, combining image and pose information to generate realistic images. It also enables 3D reconstruction by optimizing a neural field with priors from diffusion models. The method is evaluated on various datasets, including Google Scanned Objects and RTMV, demonstrating its effectiveness in generating high-fidelity images and reconstructing 3D shapes. The approach is robust to different surface materials and geometries, and can handle in-the-wild images and text-to-image generated images. The results show that Zero-1-to-3 significantly outperforms state-of-the-art methods in both tasks, achieving high-quality outputs with strong generalization capabilities.