RRepoGEO

REPOGEO REPORT · LITE

VAST-AI-Research/UniRig

Default branch main · commit 6793c664 · scanned 6/29/2026, 9:08:04 AM

GitHub: 1,622 stars · 158 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 VAST-AI-Research/UniRig, 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
    Clarify UniRig's identity as a neural network research framework in the README's opening

    Why:

    CURRENT
    This repository contains the official implementation for the **SIGGRAPH'25 (TOG) UniRig** framework, a unified solution for automatic 3D model rigging, developed by Tsinghua University and Tripo.
    COPY-PASTE FIX
    This repository presents UniRig, the official **SIGGRAPH'25 (TOG) research framework** that introduces a novel, unified **neural network approach** for automatic 3D model rigging, developed by Tsinghua University and Tripo.
  • mediumtopics#2
    Add more specific, research-oriented topics

    Why:

    CURRENT
    animation, auto-rigging, autoregressive, computer-graphics
    COPY-PASTE FIX
    animation, auto-rigging, autoregressive, computer-graphics, neural-networks, deep-learning, 3d-rigging, research-project, siggraph
  • lowabout#3
    Refine the repository description to emphasize its research nature

    Why:

    CURRENT
    [SIGGRAPH 2025] One Model to Rig Them All: Diverse Skeleton Rigging with UniRig
    COPY-PASTE FIX
    [SIGGRAPH 2025] UniRig: A research framework for diverse skeleton rigging with a unified neural network model.

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 VAST-AI-Research/UniRig
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Mixamo
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Mixamo · recommended 2×
  2. AccuRig · recommended 2×
  3. Character Creator · recommended 2×
  4. AutoRig Pro · recommended 1×
  5. Rokoko Studio · recommended 1×
  • CATEGORY QUERY
    How can I automatically generate skeletons and skinning for diverse 3D character models?
    you: not recommended
    AI recommended (in order):
    1. Mixamo
    2. AutoRig Pro
    3. AccuRig
    4. Character Creator
    5. Rokoko Studio
    6. ZBrush
    7. Maya

    AI recommended 7 alternatives but never named VAST-AI-Research/UniRig. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best methods for automating the rigging process for 3D animation assets?
    you: not recommended
    AI recommended (in order):
    1. Auto-Rig Pro
    2. Mixamo
    3. Advanced Skeleton
    4. Rigify
    5. Character Creator
    6. AccuRig
    7. Rapid Rig Modular

    AI recommended 7 alternatives but never named VAST-AI-Research/UniRig. 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 VAST-AI-Research/UniRig?
    pass
    AI named VAST-AI-Research/UniRig explicitly

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

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

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

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VAST-AI-Research/UniRig — 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