# cvlab-columbia/zero123

Zero-1-to-3: Zero-shot One Image to 3D Object (ICCV 2023)

Repository: https://github.com/cvlab-columbia/zero123
Canonical: https://ross.abutalabs.com/products/zero123
Homepage: https://zero123.cs.columbia.edu/
Language: Python
License: MIT
License Family: permissive
Topics: image-to-3d, novel-view-synthesis, single-view-reconstruction, stable-diffusion, zero-shot
Last push: 2023-12-05T09:55:45+00:00

## Health v2 (maintenance only)
Score: 30/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 90
- inputs: {"age_days": 1265, "days_push": 1002, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3058, forks 221 (observed 2026-08-28T04:07:41.055985+00:00)

## What it is
Zero-1-to-3 is a research codebase and pretrained diffusion model from Columbia CVLab that changes the camera viewpoint of an object from a single RGB image, enabling zero-shot novel view synthesis. It can also be used for single-view 3D reconstruction by training a NeRF with its viewpoint-conditioned diffusion model.

## Use cases
- generate new views of an object from a single photo
- reconstruct a 3D model from one image
- synthesize novel camera angles of objects zero-shot
- run viewpoint-conditioned stable diffusion inference
- train a NeRF using diffusion priors
- use zero123 weights in threestudio or stable-dreamfusion

## When to choose
- you need zero-shot novel view synthesis from a single image
- you want a pretrained checkpoint for image-to-3D research
- you have a GPU with ~22GB VRAM (RTX 3090/4090)
- you want to integrate viewpoint diffusion into NeRF or SDS pipelines

## When to avoid
- you need production-grade, actively maintained software
- you lack a high-VRAM GPU
- you need fast real-time inference
- you need a polished end-user application rather than research code

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, image-processing, stable-diffusion, computer-vision
- domain: computer-vision, deep-learning, artificial-intelligence, graphics
- platform: python
- tags: image-to-3d, novel-view-synthesis, single-view-reconstruction, diffusion-models, zero-shot, nerf, iccv-2023, research-code, gpu, linux

## Member repositories
- cvlab-columbia/zero123 (main) score 30

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:41.055985+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:28:27.643720+00:00, confidence not recorded.
  - readme: https://github.com/cvlab-columbia/zero123 (fetched 2026-08-28T04:07:41.055985+00:00, sha c357f133aaa3)
  - homepage: https://zero123.cs.columbia.edu/ (fetched 2026-08-29T09:43:46.640218+00:00, sha 3d2c178b475d)
- Data as of 2026-08-30T08:39:29.467469+00:00.