# 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  
**arXiv:** [2303.11328](https://arxiv.org/abs/2303.11328)  
**Date:** 2023-03-20  
**Summary page:** https://www.summarizepaper.com/en/paper/2303.11328v1/

## Key points

- Authors introduced a novel framework called "Zero-1-to-3" for manipulating camera viewpoint based on a single RGB image.
- The approach leverages geometric priors learned by large-scale diffusion models to enable novel view synthesis in an under-constrained setting.
- Key innovation is the development of a conditional diffusion model using synthetic data to control relative camera viewpoint parameters.
- Model exhibits robust zero-shot generalization capabilities, extending applicability to out-of-distribution datasets and real-world images like impressionist paintings.
- Viewpoint-conditioned diffusion methodology can be used for 3D reconstruction tasks with only one input image.
- Demonstrated through experiments that the approach significantly outperforms existing state-of-the-art models for single-view 3D reconstruction and novel view synthesis.
- Represents a significant advancement in computer vision research by showcasing how leveraging geometric priors and conditional diffusion models can facilitate accurate manipulation of object viewpoints from limited visual input.

## Summary

In their paper titled "Zero-1-to-3: Zero-shot One Image to 3D Object," authors Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick introduce a novel framework for manipulating the camera viewpoint of an object based on a single RGB image. The proposed approach, Zero-1-to-3, leverages geometric priors learned by large-scale diffusion models from natural images to enable novel view synthesis in an under-constrained setting. The key innovation lies in the development of a conditional diffusion model that utilizes a synthetic dataset to learn the parameters controlling the relative camera viewpoint. This enables the generation of new images depicting the same object from different perspectives following a specified camera transformation. Despite being trained on synthetic data, the model exhibits robust zero-shot generalization capabilities. This extends its applicability to out-of-distribution datasets and diverse real-world images, including impressionist paintings. Moreover, the viewpoint-conditioned diffusion methodology introduced in this work can also be employed for 3D reconstruction tasks using only a single input image. Through qualitative and quantitative experiments, the authors demonstrate that their approach significantly outperforms existing state-of-the-art models for single-view 3D reconstruction and novel view synthesis by harnessing Internet-scale pre-training. Overall,"Zero-1-to-3" represents a significant advancement in computer vision research by showcasing how leveraging geometric priors and conditional diffusion models can facilitate accurate and efficient manipulation of object viewpoints from limited visual input. The demonstrated performance improvements underscore the potential of this framework for various applications requiring precise control over camera transformations and 3D scene reconstruction from single images.