RRepoGEO

REPOGEO REPORT · LITE

Vchitect/VBench

Default branch master · commit 45e79ec1 · scanned 5/19/2026, 8:02:07 PM

GitHub: 1,629 stars · 118 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
40 /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
3 / 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 Vchitect/VBench, 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
    Strengthen README's opening sentence to clarify its role as a video generation benchmark suite

    Why:

    CURRENT
    This repository provides unified implementations for the **VBench series** of works, supporting comprehensive evaluation of video generative models across a wide spectrum of capabilities and settings.
    COPY-PASTE FIX
    VBench is a comprehensive benchmark suite and evaluation framework for video generative models, offering unified implementations to objectively assess their quality and capabilities across a wide spectrum of settings.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    aigc, benchmark, dataset, evaluation-kit, gen-ai, stable-diffusion, text-to-video, video-generation
    COPY-PASTE FIX
    aigc, benchmark, dataset, evaluation-kit, gen-ai, stable-diffusion, text-to-video, video-generation, video-generation-benchmark, evaluation-framework, video-quality-assessment, text-to-video-evaluation
  • lowreadme#3
    Add a 'Comparison' section to differentiate VBench from other tools

    Why:

    COPY-PASTE FIX
    ## :mag: Comparison with Other Evaluation Tools
(Add content here explaining how VBench differs from generic image metrics like FID, video feature extractors like S3D/I3D, or general CV libraries like OpenCV, emphasizing its role as a comprehensive, multi-dimensional benchmark suite specifically for video generation models.)

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 Vchitect/VBench
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenCV
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenCV · recommended 2×
  2. PyTorch-FID · recommended 1×
  3. Clean-FID · recommended 1×
  4. S3D · recommended 1×
  5. I3D · recommended 1×
  • CATEGORY QUERY
    How can I objectively assess the quality and capabilities of different video generation models?
    you: not recommended
    AI recommended (in order):
    1. PyTorch-FID
    2. Clean-FID
    3. S3D
    4. I3D
    5. TensorFlow Hub
    6. PyTorch Hub
    7. LPIPS (richzhang/PerceptualSimilarity)
    8. DISTS (dingkeyan93/DISTS)
    9. Amazon Mechanical Turk (MTurk)
    10. Prolific
    11. React
    12. Vue.js
    13. Flask
    14. Django
    15. Miro
    16. Figma
    17. OpenCV
    18. DeepSort
    19. ByteTrack

    AI recommended 19 alternatives but never named Vchitect/VBench. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools exist to benchmark text-to-video AI models for comprehensive performance comparison?
    you: not recommended
    AI recommended (in order):
    1. OpenCV
    2. scikit-image
    3. pytorch-fid
    4. clean-fid
    5. OpenAI CLIP
    6. OpenCLIP
    7. Matplotlib
    8. Seaborn
    9. Plotly
    10. Hugging Face Evaluate Library
    11. FFmpeg
    12. VMAF (Video Multi-Method Assessment Fusion)
    13. PyTorchVideo
    14. Google Cloud AI Platform Data Labeling
    15. Amazon Mechanical Turk
    16. Google Forms
    17. SurveyMonkey

    AI recommended 17 alternatives but never named Vchitect/VBench. 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 Vchitect/VBench?
    pass
    AI named Vchitect/VBench explicitly

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

  • If a team adopts Vchitect/VBench in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Vchitect/VBench 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 Vchitect/VBench solve, and who is the primary audience?
    pass
    AI named Vchitect/VBench explicitly

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

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Vchitect/VBench — 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