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

• URL: http://arxiv.org/abs/2303.11328v1
• Date: Mon, 20 Mar 2023 17:59:50 GMT
• Title: Zero-1-to-3: Zero-shot One Image to 3D Object
• Authors: Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, Carl Vondrick
• Abstract summary: We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image. Our conditional diffusion model uses a synthetic dataset to learn controls of the relative camera viewpoint. Our method significantly outperforms state-of-the-art single-view 3D reconstruction and novel view synthesis models by leveraging Internet-scale pre-training.
• Score: 30.455300183998247
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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