RRepoGEO

REPOGEO REPORT · LITE

AmmarkoV/SAM3DBody-cpp

Default branch main · commit 4acc9e8a · scanned 6/15/2026, 8:03:20 PM

GitHub: 523 stars · 72 forks

AI VISIBILITY SCORE
27 /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
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 AmmarkoV/SAM3DBody-cpp, 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 the README's first sentence to highlight core value

    Why:

    CURRENT
    Standalone C++ inference engine for **SAM-3D-Body** — zero Python dependency at runtime.
    COPY-PASTE FIX
    **SAM3DBody-cpp** is a pure C++ library for real-time, multi-person 3D full-body pose estimation from video, directly generating BVH motion capture data with zero Python dependency at runtime.
  • mediumabout#2
    Enhance the 'About' description for better keyword matching

    Why:

    CURRENT
    Real-time 3D full-body reconstruction from a single camera, Multiperson BVH output, Pure C++ runtime, ONNX + ggml, 70-joint skeleton with hands.
    COPY-PASTE FIX
    Pure C++ library for real-time, multi-person 3D full-body pose estimation from video, generating BVH motion capture data with 70-joint skeletons (including hands) via ONNX + ggml.
  • lowreadme#3
    Add a 'Key Differentiators' section to the README

    Why:

    COPY-PASTE FIX
    ## Key Differentiators
    
    Unlike many general-purpose or Python-heavy pose estimation frameworks, SAM3DBody-cpp offers:
    
    *   **Pure C++ Runtime:** Zero Python dependencies for production deployment, ensuring high performance and minimal overhead.
    *   **Direct BVH Motion Capture Output:** Generate standard BVH files per person, ready for immediate use in Blender, DCC tools, or game engines.
    *   **Real-time Multi-person Tracking:** Optimized for live video feeds, providing stable identity tracking across frames.
    *   **Comprehensive 3D Body & Hand Skeleton:** Outputs 70-joint skeletons, including detailed hand keypoints, for full-body animation.

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 AmmarkoV/SAM3DBody-cpp
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
CMU-Perceptual-Computing-Lab/openpose
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. CMU-Perceptual-Computing-Lab/openpose · recommended 1×
  2. google/mediapipe · recommended 1×
  3. MVIG-SJTU/AlphaPose · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. opencv/opencv · recommended 1×
  • CATEGORY QUERY
    Looking for a C++ library for real-time multi-person 3D pose estimation from video.
    you: not recommended
    AI recommended (in order):
    1. OpenPose (CMU-Perceptual-Computing-Lab/openpose)
    2. MediaPipe Pose (google/mediapipe)
    3. AlphaPose (MVIG-SJTU/AlphaPose)
    4. LibTorch (pytorch/pytorch)
    5. OpenCV (opencv/opencv)

    AI recommended 5 alternatives but never named AmmarkoV/SAM3DBody-cpp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to generate BVH motion capture data from a single camera video feed?
    you: not recommended
    AI recommended (in order):
    1. Plask
    2. DeepMotion Animate 3D
    3. Mixamo
    4. OpenPose
    5. scipy
    6. numpy
    7. AlphaPose
    8. Blender
    9. Rigify
    10. Rokoko Studio Live
    11. Adobe After Effects

    AI recommended 11 alternatives but never named AmmarkoV/SAM3DBody-cpp. 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 AmmarkoV/SAM3DBody-cpp?
    pass
    AI did not name AmmarkoV/SAM3DBody-cpp — 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 AmmarkoV/SAM3DBody-cpp in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AmmarkoV/SAM3DBody-cpp 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 AmmarkoV/SAM3DBody-cpp solve, and who is the primary audience?
    pass
    AI did not name AmmarkoV/SAM3DBody-cpp — 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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AmmarkoV/SAM3DBody-cpp — 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