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# Zero-1-to-3 (Image to 3D)

**Zero-1-to-3 (Image to 3D)**

We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image. 

{% embed url="<https://zero123.cs.columbia.edu/>" %}
<https://zero123.cs.columbia.edu/>
{% endembed %}

**Hugging Face Demo:**

{% embed url="<https://huggingface.co/spaces/cvlab/zero123-live>" %}

<https://huggingface.co/spaces/ysharma/Zero123PlusDemo>

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.&#x20;

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.&#x20;

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.&#x20;

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.