RRepoGEO

REPOGEO REPORT · LITE

colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers

Default branch main · commit 98300fb7 · scanned 6/30/2026, 1:32:38 PM

GitHub: 1,110 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
17 /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
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 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

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, continuously updated collection of recent research papers on trajectory and motion prediction, including LLM-based approaches, from major AI/robotics conferences and arXiv.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    trajectory-prediction, motion-prediction, research-papers, awesome-list, deep-learning, computer-vision, robotics, generative-models, graph-neural-networks, llm-based-prediction
  • mediumreadme#3
    Reposition the README's opening to explicitly state its 'awesome list' nature

    Why:

    CURRENT
    # Trajectory/Motion Prediction Papers

**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: A Curated List

**This repository is a curated and continuously updated awesome list of recent research papers on trajectory and motion prediction. It covers topics like generative models, graph neural networks, and LLM-based approaches. If you find this collection 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
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. arXiv · recommended 1×
  2. Google Scholar · recommended 1×
  3. OpenReview · recommended 1×
  4. CVPR · recommended 1×
  5. ICCV · recommended 1×
  • CATEGORY QUERY
    Where can I find recent research papers on human trajectory and motion prediction?
    you: not recommended
    AI recommended (in order):
    1. arXiv
    2. Google Scholar
    3. OpenReview
    4. CVPR
    5. ICCV
    6. ECCV
    7. ICRA
    8. IROS
    9. RSS
    10. GitHub

    AI recommended 10 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 cutting-edge techniques for predicting object trajectories in dynamic environments?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow (tensorflow/tensorflow)
    2. PyTorch (pytorch/pytorch)
    3. Keras (keras-team/keras)
    4. Hugging Face Transformers library (huggingface/transformers)
    5. PyTorch Geometric (PyG) (pyg-team/pytorch_geometric)
    6. Deep Graph Library (DGL) (dmlc/dgl)
    7. FilterPy (rlabbe/filterpy)
    8. OpenCV (opencv/opencv)
    9. GPyTorch (cornellius-gp/gpytorch)
    10. GPflow (GPflow/GPflow)
    11. DeepXDE (lululxvi/deepxde)
    12. Stable Baselines3 (DLR-RM/stable-baselines3)
    13. Ray RLlib (ray-project/ray)

    AI recommended 13 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 named colorfulfuture/Awesome-Trajectory-Motion-Prediction-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 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.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers.svg)](https://repogeo.com/en/r/colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers)
HTML
<a href="https://repogeo.com/en/r/colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers"><img src="https://repogeo.com/badge/colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers — 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
colorfulfuture/Awesome-Trajectory-Motion-Prediction-Papers — RepoGEO report