RRepoGEO

REPOGEO REPORT · LITE

Stable-X/StableNormal

Default branch main · commit 594b9346 · scanned 5/31/2026, 3:02:47 AM

GitHub: 774 stars · 38 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 Stable-X/StableNormal, 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
  • highabout#1
    Update repository description to explicitly state 'computer vision' and 'surface normal estimation'

    Why:

    CURRENT
    [SIGGRAPH Asia 2024 (Journal Track)] StableNormal: Reducing Diffusion Variance for Stable and Sharp Normal
    COPY-PASTE FIX
    [SIGGRAPH Asia 2024 (Journal Track)] StableNormal: A computer vision project for stable and sharp surface normal estimation by reducing diffusion variance.
  • highreadme#2
    Add 'surface' to 'normal estimation' in the README's first sentence

    Why:

    CURRENT
    We propose StableNormal, which tailors the diffusion priors for monocular normal estimation.
    COPY-PASTE FIX
    We propose StableNormal, which tailors the diffusion priors for monocular *surface* normal estimation.
  • mediumhomepage#3
    Add the project homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://stable-x.github.io/StableNormal

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 Stable-X/StableNormal
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
MiDaS
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. MiDaS · recommended 2×
  2. ZoeDepth · recommended 2×
  3. Hugging Face Transformers library · recommended 1×
  4. PyTorch · recommended 1×
  5. OmniCV · recommended 1×
  • CATEGORY QUERY
    How to get stable and sharp surface normals from a single image?
    you: not recommended
    AI recommended (in order):
    1. MiDaS
    2. Hugging Face Transformers library
    3. PyTorch
    4. ZoeDepth
    5. OmniCV
    6. Pix2Pix
    7. Open3D
    8. NumPy

    AI recommended 8 alternatives but never named Stable-X/StableNormal. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need a robust zero-shot method for accurate surface normal estimation in varied environments.
    you: not recommended
    AI recommended (in order):
    1. Diffusion-Normals
    2. Zero-Shot Normal Diffusion
    3. OmniNormal
    4. MiDaS
    5. ZoeDepth
    6. DPT
    7. NormalGAN

    AI recommended 7 alternatives but never named Stable-X/StableNormal. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 Stable-X/StableNormal?
    pass
    AI named Stable-X/StableNormal explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts Stable-X/StableNormal in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Stable-X/StableNormal 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 Stable-X/StableNormal solve, and who is the primary audience?
    pass
    AI named Stable-X/StableNormal explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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Stable-X/StableNormal — 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