Zero-1-to-3: Zero-shot One Image to 3D Object

We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image. Our viewpoint-conditioned diffusion approach can further be used for the task of 3D reconstruction from a single image.

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• 1 To perform novel view synthesis in this under-constrained setting, we capitalize on the geometric priors that large-scale diffusion models learn about natural images.
• 2 Our conditional diffusion model uses a synthetic dataset to learn controls of the relative camera viewpoint, which allow new images to be generated of the same object under a specified camera transformation.
• 3 Even though it is trained on a synthetic dataset, our model retains a strong zero-shot generalization ability to out-of-distribution datasets as well as in-the-wild images, including impressionist paintings.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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