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

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arxiv 2303.11328 v1 pith:I5NA7QDT submitted 2023-03-20 cs.CV cs.GRcs.RO

Ruoshi Liu , Rundi Wu , Basile Van Hoorick , Pavel Tokmakov , Sergey Zakharov , Carl Vondrick This is my paper
classification cs.CVcs.GRcs.RO
keywords cameradiffusionimageimagesobjectdatasetlearnmodel
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We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image. 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. 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. 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. Our viewpoint-conditioned diffusion approach can further be used for the task of 3D reconstruction from a single image. Qualitative and quantitative experiments show that our method significantly outperforms state-of-the-art single-view 3D reconstruction and novel view synthesis models by leveraging Internet-scale pre-training.

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