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We introduce Zero-1-to-3, a framework for changing the camera viewpoint of anobject given just a single RGB image. To perform novel view synthesis in thisunder-constrained setting, we capitalize on the geometric priors thatlarge-scale diffusion models learn about natural images. Our conditionaldiffusion model uses a synthetic dataset to learn controls of the relativecamera viewpoint, which allow new images to be generated of the same objectunder a specified camera transformation. Even though it is trained on asynthetic dataset, our model retains a strong zero-shot generalization abilityto out-of-distribution datasets as well as in-the-wild images, includingimpressionist paintings. Our viewpoint-conditioned diffusion approach canfurther be used for the task of 3D reconstruction from a single image.Qualitative and quantitative experiments show that our method significantlyoutperforms state-of-the-art single-view 3D reconstruction and novel viewsynthesis models by leveraging Internet-scale pre-training.

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