RRepoGEO

REPOGEO REPORT · LITE

colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers

Default branch main · commit 98300fb7 · scanned 5/19/2026, 6:03:01 AM

GitHub: 1,102 stars · 94 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
10 /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
0 / 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 colorfulfuture/Awesome-Trajectory-Motion-Prediction-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

2 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, actively updated 'Awesome List' of recent research papers on trajectory and motion prediction, including LLM-based approaches.
  • mediumreadme#2
    Refine the README's opening to clarify its 'Awesome List' nature

    Why:

    CURRENT
    # Trajectory/Motion Prediction Papers

[](https://awesome.re)

**Collecting Recent Trajectory and Motion Prediction Papers. Keep Updating.
If you find this repo useful, please ⭐️ star it and feel free to submit a pull request to contribute more papers!**
    COPY-PASTE FIX
    # Awesome Trajectory/Motion Prediction Papers

[](https://awesome.re)

A curated and actively updated 'Awesome List' collecting recent research papers on trajectory and motion prediction, including cutting-edge LLM-based approaches. If you find this repo useful, please ⭐️ star it and feel free to submit a pull request to contribute more papers!

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 colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
arXiv.org
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv.org · recommended 1×
  2. Google Scholar · recommended 1×
  3. CVF Open Access (Computer Vision Foundation) · recommended 1×
  4. NeurIPS Proceedings (Neural Information Processing Systems) · recommended 1×
  5. ICML Proceedings (International Conference on Machine Learning) · recommended 1×
  • CATEGORY QUERY
    Where can I find recent research papers on human motion and object trajectory prediction?
    you: not recommended
    AI recommended (in order):
    1. arXiv.org
    2. Google Scholar
    3. CVF Open Access (Computer Vision Foundation)
    4. NeurIPS Proceedings (Neural Information Processing Systems)
    5. ICML Proceedings (International Conference on Machine Learning)
    6. Robotics: Science and Systems (RSS) Proceedings
    7. IEEE Xplore Digital Library
    8. ACM Digital Library

    AI recommended 8 alternatives but never named colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the latest advancements in AI models for predicting future motion paths?
    you: not recommended
    AI recommended (in order):
    1. TrajFormer
    2. MotionGPT
    3. Social-GAN
    4. STGAT
    5. PECNet
    6. Social-LSTM
    7. GRIP
    8. TrajDiff
    9. TrajPINN
    10. AlphaPilot
    11. Waymo's Motion Planning

    AI recommended 11 alternatives but never named colorfulfuture/Awesome-Trajectory-Motion-Prediction-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
    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 colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers?
    pass
    AI did not name colorfulfuture/Awesome-Trajectory-Motion-Prediction-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 colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name colorfulfuture/Awesome-Trajectory-Motion-Prediction-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?

  • In one sentence, what problem does the repo colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers solve, and who is the primary audience?
    pass
    AI did not name colorfulfuture/Awesome-Trajectory-Motion-Prediction-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?

Embed your GEO score

Drop this badge into the README of colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers. 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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colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers — 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