REPOGEO REPORT · LITE

google-deepmind/tapnet

Default branch main · commit d1012b6b · scanned 5/18/2026, 6:33:21 AM

GitHub: 1,887 stars · 179 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 google-deepmind/tapnet, 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 README H1 to specify category and advanced nature

Why:

CURRENT

# Tracking Any Point (TAP)

COPY-PASTE FIX

# Tracking Any Point (TAP): State-of-the-Art Deep Learning for Visual Point Tracking in Videos and Robotics

highreadme#2

Add explicit domain clarification to README intro

Why:

CURRENT

Welcome to the official Google Deepmind repository for Tracking Any Point (TAP), home of the TAP-Vid and TAPVid-3D Datasets, our top-performing TAPIR model, and our RoboTAP extension.

COPY-PASTE FIX

Welcome to the official Google Deepmind repository for Tracking Any Point (TAP). This project provides state-of-the-art deep learning solutions for robust visual point tracking across video frames. It is not related to audio processing or text-to-audio generation. Here you will find the TAP-Vid and TAPVid-3D Datasets, our top-performing TAPIR model, and the RoboTAP extension for real-world robotics manipulation.

mediumtopics#3

Expand topics to include more specific keywords

Why:

CURRENT

benchmark, computer-vision, deep-learning, point-tracking, robotics

COPY-PASTE FIX

benchmark, computer-vision, deep-learning, point-tracking, robotics, video-tracking, visual-tracking, state-of-the-art-ml, real-world-robotics

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 google-deepmind/tapnet

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

OpenCV

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

OpenCV · recommended 2×
DeepLabCut · recommended 1×
AlphaPose · recommended 1×
TrackEval · recommended 1×
BoT-SORT · recommended 1×

CATEGORY QUERY
How to accurately track arbitrary points across video frames for computer vision tasks?
you: not recommended
AI recommended (in order):
1. OpenCV
2. DeepLabCut
3. AlphaPose
4. TrackEval
5. BoT-SORT
6. ByteTrack
7. SORT
AI recommended 7 alternatives but never named google-deepmind/tapnet. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are effective methods for using visual point tracking in real-world robotics manipulation?
you: not recommended
AI recommended (in order):
1. ArUco Markers
2. AprilTags
3. OpenCV
4. cv::calcOpticalFlowPyrLK
5. cv::GoodFeaturesToTrack
6. ORB
7. SuperGlue (magicleap/SuperGluePretrainedNetwork)
8. DINOv2 (facebookresearch/dinov2)
9. ORB-SLAM3 (UZ-SLAM/ORB_SLAM3)
10. RTAB-Map (introlab/rtabmap)
11. KCF
12. CSRT
13. DeepSORT (nwojke/deep_sort)
14. ByteTrack (ifzhang/ByteTrack)
AI recommended 14 alternatives but never named google-deepmind/tapnet. 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 google-deepmind/tapnet?
pass
AI named google-deepmind/tapnet explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts google-deepmind/tapnet in production, what risks or prerequisites should they evaluate first?
pass
AI named google-deepmind/tapnet 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 google-deepmind/tapnet solve, and who is the primary audience?
pass
AI named google-deepmind/tapnet explicitly
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 google-deepmind/tapnet. 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)

[![RepoGEO](https://repogeo.com/badge/google-deepmind/tapnet.svg)](https://repogeo.com/en/r/google-deepmind/tapnet)

HTML

<a href="https://repogeo.com/en/r/google-deepmind/tapnet"><img src="https://repogeo.com/badge/google-deepmind/tapnet.svg" alt="RepoGEO" /></a>

Pro

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google-deepmind/tapnet — 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