RRepoGEO

REPOGEO REPORT · LITE

anliyuan/Ultralight-Digital-Human

Default branch master · commit 8d82cedb · scanned 5/19/2026, 1:09:01 AM

GitHub: 2,497 stars · 366 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
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
2 / 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 anliyuan/Ultralight-Digital-Human, 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

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

OVERALL DIRECTION
  • highlicense#1
    Create a LICENSE file

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root. While the specific license is up to the maintainer, common choices for open-source projects include `MIT` or `Apache-2.0`. If a custom license is intended, explicitly state its terms in the `LICENSE` file and reference it in the README.
  • highreadme#2
    Clarify README's opening to emphasize "model" and "developer-focused"

    Why:

    CURRENT
    # Ultralight Digital Human
...
A Ultralight Digital Human model can run on mobile devices in real time!!!
    COPY-PASTE FIX
    # Ultralight Digital Human: Real-time, Mobile-Optimized Model for Developers

This is an ultralight digital human model designed for real-time performance on mobile devices. To our knowledge, this is the first open-source model of its kind, offering unparalleled efficiency for integrating virtual avatars into resource-constrained environments.

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 anliyuan/Ultralight-Digital-Human
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ready Player Me
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Ready Player Me · recommended 2×
  2. Mixamo · recommended 2×
  3. Unity · recommended 1×
  4. AR Foundation · recommended 1×
  5. Cinemachine · recommended 1×
  • CATEGORY QUERY
    How to create a real-time virtual avatar that performs well on mobile devices?
    you: not recommended
    AI recommended (in order):
    1. Ready Player Me
    2. Unity
    3. AR Foundation
    4. Cinemachine
    5. URP
    6. HDRP
    7. Unreal Engine
    8. Mixamo
    9. Blender
    10. Eevee
    11. 8th Wall
    12. Three.js
    13. GLTF/GLB

    AI recommended 13 alternatives but never named anliyuan/Ultralight-Digital-Human. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an efficient, low-latency digital human solution for streaming applications on resource-constrained devices.
    you: not recommended
    AI recommended (in order):
    1. Ready Player Me
    2. MetaHuman Animator
    3. Vroid Studio
    4. DeepMotion
    5. Mixamo
    6. Avatar SDK

    AI recommended 6 alternatives but never named anliyuan/Ultralight-Digital-Human. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 anliyuan/Ultralight-Digital-Human?
    pass
    AI named anliyuan/Ultralight-Digital-Human explicitly

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

  • If a team adopts anliyuan/Ultralight-Digital-Human in production, what risks or prerequisites should they evaluate first?
    pass
    AI named anliyuan/Ultralight-Digital-Human 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 anliyuan/Ultralight-Digital-Human solve, and who is the primary audience?
    pass
    AI did not name anliyuan/Ultralight-Digital-Human — likely talking about a different project

    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 anliyuan/Ultralight-Digital-Human. 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)
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anliyuan/Ultralight-Digital-Human — 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