RRepoGEO

REPOGEO REPORT · LITE

Tencent-Hunyuan/Hunyuan3D-Omni

Default branch main · commit 4d47c0cc · scanned 6/11/2026, 3:43:14 AM

GitHub: 586 stars · 53 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
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 Tencent-Hunyuan/Hunyuan3D-Omni, 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 the README's opening paragraph to clarify its generative AI framework identity

    Why:

    CURRENT
    Hunyuan3D-Omni is a unified framework for the controllable generation of 3D assets, which inherits the structure of Hunyuan3D 2.1. In contrast, Hunyuan3D-Omni constructs a unified control encoder to introduce additional control signals, including point cloud, voxel, skeleton, and bounding box.
    COPY-PASTE FIX
    Hunyuan3D-Omni is a cutting-edge **generative AI framework** for controllable 3D asset creation, building upon Hunyuan3D 2.1. It introduces a unified control encoder to enable precise generation from diverse signals like point clouds, voxels, skeletons, and bounding boxes, offering unparalleled control for researchers and developers.
  • mediumreadme#2
    Explicitly state the core differentiator in the README

    Why:

    COPY-PASTE FIX
    Add a new paragraph after the introduction: "What sets Hunyuan3D-Omni apart is its **unified framework** approach, seamlessly integrating diverse multimodal conditional controls—from bounding boxes and poses to point clouds and voxels—into a single system. This enables high-quality 3D asset generation with a level of precision and flexibility unmatched by many specialized generative models."
  • lowlicense#3
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README: "## License\nThis project is released under the terms specified in the [LICENSE](LICENSE) file. Please review the file for full details on usage and distribution."

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 Tencent-Hunyuan/Hunyuan3D-Omni
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Blender
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Blender · recommended 1×
  2. Autodesk Maya · recommended 1×
  3. Unreal Engine · recommended 1×
  4. MetaHuman Creator · recommended 1×
  5. Control Rig · recommended 1×
  • CATEGORY QUERY
    How can I generate 3D assets with precise control over bounding boxes or skeletal poses?
    you: not recommended
    AI recommended (in order):
    1. Blender
    2. Autodesk Maya
    3. Unreal Engine
    4. MetaHuman Creator
    5. Control Rig
    6. Unity
    7. Animation Rigging package (Unity-Technologies/com.unity.animation.rigging)
    8. ZBrush
    9. Transpose Master
    10. Substance 3D Modeler

    AI recommended 10 alternatives but never named Tencent-Hunyuan/Hunyuan3D-Omni. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are unified frameworks for creating 3D models from diverse multimodal conditional inputs?
    you: not recommended
    AI recommended (in order):
    1. DreamFusion
    2. Magic3D
    3. Stable Diffusion 3D
    4. Fantasia3D
    5. GET3D
    6. Luma AI
    7. Instant NGP
    8. Nerfstudio (nerfstudio-project/nerfstudio)
    9. Mip-NeRF 360
    10. Kaedim
    11. Spline

    AI recommended 11 alternatives but never named Tencent-Hunyuan/Hunyuan3D-Omni. 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 Tencent-Hunyuan/Hunyuan3D-Omni?
    pass
    AI did not name Tencent-Hunyuan/Hunyuan3D-Omni — 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 Tencent-Hunyuan/Hunyuan3D-Omni in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Tencent-Hunyuan/Hunyuan3D-Omni 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 Tencent-Hunyuan/Hunyuan3D-Omni solve, and who is the primary audience?
    pass
    AI named Tencent-Hunyuan/Hunyuan3D-Omni explicitly

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

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Tencent-Hunyuan/Hunyuan3D-Omni — 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