REPOGEO REPORT · LITE

taco-group/SparkVSR

Default branch main · commit 1a35b060 · scanned 6/1/2026, 5:58:17 AM

GitHub: 627 stars · 69 forks

AI VISIBILITY SCORE

33 /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

2 / 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 taco-group/SparkVSR, 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

Add a clarifying note to the README's opening about project scope

Why:

CURRENT

<h1>SparkVSR: Interactive Video Super-Resolution via Sparse Keyframe Propagation</h1>

COPY-PASTE FIX

<h1>SparkVSR: Interactive Video Super-Resolution via Sparse Keyframe Propagation</h1>
<p>
  <strong>Note: SparkVSR is a novel interactive video super-resolution framework and is not related to Apache Spark or distributed computing.</strong>
</p>

mediumtopics#2

Refine and expand repository topics for better categorization

Why:

CURRENT

artificial-intelligence, generative-ai, generative-models, image-processing, llm, machine-learning, restoration, super-resolution, vfx, video, video-editing, video-generation, video-processing, video-restoration, video-streaming, vlm

COPY-PASTE FIX

artificial-intelligence, generative-ai, generative-models, image-processing, machine-learning, restoration, super-resolution, vfx, video, video-editing, video-generation, video-processing, video-restoration, interactive-ai, human-in-the-loop, artifact-correction, keyframe-control

lowcomparison#3

Add a 'Why SparkVSR?' section to the README highlighting differentiators

Why:

COPY-PASTE FIX

### Why SparkVSR? Our Differentiators

Unlike black-box video super-resolution methods, SparkVSR offers **interactive control** through sparse keyframe propagation. This allows users to reliably correct unexpected artifacts and guide the super-resolution process, providing a level of user agency not found in traditional VSR models like Topaz Video AI or BasicSR. Our approach focuses on user-guided quality improvement rather than purely automated output.

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 taco-group/SparkVSR

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Topaz Video AI

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Topaz Video AI · recommended 2×
DaVinci Resolve Studio · recommended 1×
Upscayl · recommended 1×
Waifu2x-caffe · recommended 1×
Waifu2x-Extension-GUI · recommended 1×

CATEGORY QUERY
Need a tool to upscale video resolution with AI, allowing interactive artifact correction.
you: not recommended
AI recommended (in order):
1. Topaz Video AI
2. DaVinci Resolve Studio
3. Upscayl
4. Waifu2x-caffe
5. Waifu2x-Extension-GUI
6. Gigapixel AI
AI recommended 6 alternatives but never named taco-group/SparkVSR. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are good generative models for video restoration and improving visual quality from low-res?
you: not recommended
AI recommended (in order):
1. BasicSR
2. Real-ESRGAN
3. GFPGAN
4. CodeFormer
5. Topaz Video AI
6. DAIN
7. SwinIR
8. EDVR
9. VSR-Transformer
AI recommended 9 alternatives but never named taco-group/SparkVSR. 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 taco-group/SparkVSR?
pass
AI did not name taco-group/SparkVSR — 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 taco-group/SparkVSR in production, what risks or prerequisites should they evaluate first?
pass
AI named taco-group/SparkVSR 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 taco-group/SparkVSR solve, and who is the primary audience?
pass
AI named taco-group/SparkVSR 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 taco-group/SparkVSR. 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/taco-group/SparkVSR.svg)](https://repogeo.com/en/r/taco-group/SparkVSR)

HTML

<a href="https://repogeo.com/en/r/taco-group/SparkVSR"><img src="https://repogeo.com/badge/taco-group/SparkVSR.svg" alt="RepoGEO" /></a>

Pro

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taco-group/SparkVSR — 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