REPOGEO REPORT · LITE

cvlab-columbia/zero123

Default branch main · commit f426883b · scanned 6/22/2026, 1:58:42 PM

GitHub: 3,052 stars · 219 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface cvlab-columbia/zero123, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Reposition README to clearly state problem and solution

Why:

COPY-PASTE FIX

Add the following sentence directly after the '### ICCV 2023' line in the README:
This repository provides the official implementation for Zero-1-to-3, a state-of-the-art method for generating multi-view consistent images and 3D objects from a single 2D input, targeting researchers and developers in computer vision and 3D AI.

mediumreadme#2

Add a 'What is Zero-1-to-3?' section to clarify its role

Why:

COPY-PASTE FIX

Add a new section titled '## What is Zero-1-to-3?' after the initial project description and before the 'Updates' section, with content similar to:
Zero-1-to-3 is a research project that provides a powerful foundation for generating multi-view consistent images and 3D objects from a single 2D input. Unlike commercial tools that offer end-to-end solutions, Zero-1-to-3 focuses on the core generative model, making it ideal for integration into custom pipelines (e.g., with Threestudio or Stable-Dreamfusion) or for research into novel view synthesis and 3D reconstruction.

lowtopics#3

Expand topics for broader keyword matching

Why:

CURRENT

image-to-3d, novel-view-synthesis, single-view-reconstruction, stable-diffusion, zero-shot

COPY-PASTE FIX

image-to-3d, novel-view-synthesis, single-view-reconstruction, stable-diffusion, zero-shot, 3d-generation, 3d-reconstruction, single-image-3d

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface cvlab-columbia/zero123

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Luma AI (Genie)

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

Luma AI (Genie) · recommended 1×
NVlabs/instant-ngp · recommended 1×
Spline · recommended 1×
Kaedim · recommended 1×
3DFY.ai · recommended 1×

CATEGORY QUERY
How can I generate a 3D model of an object given only a single 2D image?
you: not recommended
AI recommended (in order):
1. Luma AI (Genie)
2. Instant NGP (NVlabs/instant-ngp)
3. Spline
4. Kaedim
5. 3DFY.ai
6. Blender
7. ZBrush
AI recommended 7 alternatives but never named cvlab-columbia/zero123. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for an AI solution to synthesize new perspectives from one input photograph.
you: not recommended
AI recommended (in order):
1. NeRF
2. Mip-NeRF 360
3. Instant-NGP
4. K-Planes
5. 3D Gaussian Splatting
6. Midjourney
7. DALL-E 3
8. Stable Diffusion
9. ControlNet
10. MiDaS
11. Luma AI
12. OmniDreamer
AI recommended 12 alternatives but never named cvlab-columbia/zero123. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of cvlab-columbia/zero123?
pass
AI named cvlab-columbia/zero123 explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts cvlab-columbia/zero123 in production, what risks or prerequisites should they evaluate first?
pass
AI named cvlab-columbia/zero123 explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo cvlab-columbia/zero123 solve, and who is the primary audience?
pass
AI named cvlab-columbia/zero123 explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of cvlab-columbia/zero123. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/cvlab-columbia/zero123.svg)](https://repogeo.com/en/r/cvlab-columbia/zero123)

HTML

<a href="https://repogeo.com/en/r/cvlab-columbia/zero123"><img src="https://repogeo.com/badge/cvlab-columbia/zero123.svg" alt="RepoGEO" /></a>

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cvlab-columbia/zero123 — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite