RRepoGEO

REPOGEO REPORT · LITE

rlczddl/awesome-3d-human-reconstruction

Default branch master · commit a262058b · scanned 5/31/2026, 10:32:52 AM

GitHub: 953 stars · 94 forks

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 rlczddl/awesome-3d-human-reconstruction, 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
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A curated list of papers, datasets, and resources for 3D human reconstruction, including faces, bodies, hands, and related topics like NeRF and AIGC.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ["3d-human-reconstruction", "awesome-list", "computer-vision", "deep-learning", "generative-ai", "nerf", "3d-vision", "human-body", "human-face", "datasets", "papers"]
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    (Choose an appropriate open-source license like MIT or Apache-2.0 and add it to a LICENSE file in the repository root.)

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 rlczddl/awesome-3d-human-reconstruction
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Scholar
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Scholar · recommended 1×
  2. arXiv · recommended 1×
  3. CVF Open Access · recommended 1×
  4. Semantic Scholar · recommended 1×
  5. Microsoft Academic · recommended 1×
  • CATEGORY QUERY
    Where can I find academic papers and datasets on 3D human body reconstruction?
    you: not recommended
    AI recommended (in order):
    1. Google Scholar
    2. arXiv
    3. CVF Open Access
    4. Semantic Scholar
    5. Microsoft Academic
    6. AMASS (Archive of Motion Capture as Surface Shapes)
    7. 3DPW (3D Poses in the Wild)
    8. SURREAL (Synthetic hUman REalistic ALiasing)
    9. Human3.6M
    10. MPI-INF-3DHP (MPI-IS Human Pose Dataset)
    11. SCAPE (Shape Completion and Animation of People)
    12. AGORA (A Giga-scale, Optimal, and Real-time Dataset for Human Pose and Shape Estimation)

    AI recommended 12 alternatives but never named rlczddl/awesome-3d-human-reconstruction. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest research advancements in reconstructing 3D human faces from images?
    you: not recommended
    AI recommended (in order):
    1. FLAME
    2. BFM
    3. DECA
    4. EMOCA
    5. SADR
    6. HeadNeRF
    7. NeRF-W
    8. EG3D
    9. StyleNeRF
    10. ICON
    11. PIFuHD
    12. DeepSDF
    13. StyleGAN-Human
    14. 3D-GAN

    AI recommended 14 alternatives but never named rlczddl/awesome-3d-human-reconstruction. 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 rlczddl/awesome-3d-human-reconstruction?
    pass
    AI did not name rlczddl/awesome-3d-human-reconstruction — 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?

  • If a team adopts rlczddl/awesome-3d-human-reconstruction in production, what risks or prerequisites should they evaluate first?
    pass
    AI named rlczddl/awesome-3d-human-reconstruction 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 rlczddl/awesome-3d-human-reconstruction solve, and who is the primary audience?
    pass
    AI named rlczddl/awesome-3d-human-reconstruction explicitly

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

Embed your GEO score

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MARKDOWN (README)
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rlczddl/awesome-3d-human-reconstruction — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
rlczddl/awesome-3d-human-reconstruction — RepoGEO report