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.
Novel View Synthesis
Here are some uncurated inference results from in-the-wild images we tried, along with images from the Google Scanned Objects and RTMV datsets. Note that the demo allows a limited selection of rotation angles quantized by 30 degrees due to limited storage space of the hosting server. If you want to try out a fully custom demo running on a GPU server which allows you to upload your own image, please check out our code!
Single-View 3D Reconstruction
Here are results of applying Zero-1-to-3 to obtain a full 3D reconstruction from the input image shown on the left. We compare our reconstruction with state-of-the-art models in single-view 3D reconstruction.
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