Zero-1-to-3 is a research model from Zero123 that performs novel view synthesis and 3D object generation from a single input image. It learns camera viewpoint transformations, enabling it to generate plausible views of an object from angles not present in the source image.
The model is particularly suited for 3D asset prototyping, product visualization, and training data generation for downstream 3D tasks. It occupies a niche between pure image generation and full geometric reconstruction, making it a practical tool when only a reference photo is available.
Key Features
Single-image novel view synthesis across arbitrary camera angles
3D-consistent object appearance generation without depth maps
Camera pose conditioning for controlled viewpoint control
Compatible with downstream NeRF and mesh reconstruction pipelines
Generalizes across diverse object categories
Ideal Use Cases
Generating multiple product views from a single catalog photo
Creating 3D training datasets from 2D image collections
Prototyping 3D assets before full modeling
Augmenting datasets for robotics perception systems
Example Prompts for Zero-1-to-3
Technical Specifications
| Provider | Zero123 |
| Category | 3D |
| Modality | Text/Image -> 3D |
Frequently Asked Questions
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