RRepoGEO

REPOGEO REPORT · LITE

3DFaceBody/awesome-3dbody-papers

Default branch master · commit caffe07f · scanned 6/12/2026, 2:02:43 PM

GitHub: 661 stars · 72 forks

AI VISIBILITY SCORE
22 /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
1 / 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 3DFaceBody/awesome-3dbody-papers, 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
    Clarify README's opening sentence to emphasize "research papers"

    Why:

    CURRENT
    > An awesome & curated list of papers about 3D human body.
    COPY-PASTE FIX
    > A comprehensive and curated list of **research papers** focusing on 3D human body modeling, pose estimation, motion capture, and related advancements. This repository serves as a central resource for researchers and practitioners, not a software project or tool.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root. For an awesome list, a permissive license like MIT is common. Example content for MIT: `MIT License
    
    Copyright (c) [YEAR] [COPYRIGHT HOLDER]
    
    Permission is hereby granted, free of charge, to any person obtaining a copy
    of this software and associated documentation files (the "Software"), to deal
    in the Software without restriction, including without limitation the rights
    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
    copies of the Software, and to permit persons to whom the Software is
    furnished to do so, subject to the following conditions:
    
    The above copyright notice and this permission notice shall be included in all
    copies or substantial portions of the Software.
    
    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
    SOFTWARE.`
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    Set the homepage URL in the repository settings to `https://github.com/3DFaceBody/awesome-3dbody-papers` (or a dedicated GitHub Pages URL if one exists).

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 3DFaceBody/awesome-3dbody-papers
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Awesome-Human-Pose-Tracking
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome-Human-Pose-Tracking · recommended 1×
  2. Papers With Code - 3D Human Pose Estimation · recommended 1×
  3. CVF Open Access · recommended 1×
  4. Google Scholar · recommended 1×
  5. arXiv.org · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated list of research papers on 3D human body modeling?
    you: not recommended
    AI recommended (in order):
    1. Awesome-Human-Pose-Tracking
    2. Papers With Code - 3D Human Pose Estimation
    3. CVF Open Access
    4. Google Scholar
    5. arXiv.org
    6. Human Body Model (HBM) Project Page
    7. Microsoft Academic

    AI recommended 7 alternatives but never named 3DFaceBody/awesome-3dbody-papers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the key research advancements in 3D human motion capture and tracking?
    you: not recommended
    AI recommended (in order):
    1. OpenPose
    2. VIBE
    3. SMPL
    4. SMPL-X
    5. SPIN
    6. HMR
    7. FrankMocap
    8. Neural Radiance Fields (NeRF)
    9. Azure Kinect DK
    10. Perception Neuron
    11. NVIDIA Jetson platforms
    12. MediaPipe Hands/Face
    13. MANO
    14. FLAME
    15. Isaac Gym

    AI recommended 15 alternatives but never named 3DFaceBody/awesome-3dbody-papers. 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 3DFaceBody/awesome-3dbody-papers?
    pass
    AI did not name 3DFaceBody/awesome-3dbody-papers — 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 3DFaceBody/awesome-3dbody-papers in production, what risks or prerequisites should they evaluate first?
    pass
    AI named 3DFaceBody/awesome-3dbody-papers 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 3DFaceBody/awesome-3dbody-papers solve, and who is the primary audience?
    pass
    AI did not name 3DFaceBody/awesome-3dbody-papers — 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?

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