RRepoGEO

REPOGEO REPORT · LITE

Fictionarry/ER-NeRF

Default branch main · commit 0cf72d8f · scanned 5/28/2026, 5:27:21 PM

GitHub: 1,253 stars · 142 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 Fictionarry/ER-NeRF, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    neural-radiance-fields, nerf, talking-portrait, human-animation, computer-vision, iccv2023, deep-learning, pytorch
  • highreadme#2
    Strengthen the README's opening statement to emphasize specific application and efficiency

    Why:

    CURRENT
    # Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis
This is the official repository for our ICCV 2023 paper **Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis**.
    COPY-PASTE FIX
    # Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis
This repository presents ER-NeRF, an ICCV 2023 paper that introduces an efficient and region-aware Neural Radiance Field method specifically designed for synthesizing high-fidelity talking portraits. It enables realistic human face animation from audio input.
  • mediumreadme#3
    Add a 'Key Features' section to the README

    Why:

    COPY-PASTE FIX
    ## Key Features
- **High-Fidelity Talking Portraits:** Generates realistic talking head videos with precise lip synchronization and natural expressions.
- **Region-Aware Efficiency:** Optimizes NeRF rendering by focusing computational resources on relevant facial regions.
- **ICCV 2023 Paper:** Official implementation of our research presented at ICCV 2023.

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 Fictionarry/ER-NeRF
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
D-ID Creative Reality Studio
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. D-ID Creative Reality Studio · recommended 1×
  2. HeyGen · recommended 1×
  3. Synthesia · recommended 1×
  4. DeepMotion (Animate 3D) · recommended 1×
  5. SadTalker · recommended 1×
  • CATEGORY QUERY
    How to synthesize high-fidelity talking portrait videos from a single image?
    you: not recommended
    AI recommended (in order):
    1. D-ID Creative Reality Studio
    2. HeyGen
    3. Synthesia
    4. DeepMotion (Animate 3D)
    5. SadTalker
    6. Wav2Lip

    AI recommended 6 alternatives but never named Fictionarry/ER-NeRF. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are efficient neural rendering techniques for realistic human face animation?
    you: not recommended
    AI recommended (in order):
    1. NeRF
    2. Instant-NGP
    3. TensoRF
    4. K-Planes
    5. DeepFaceRendering
    6. Face-NeRF
    7. StyleGAN-XL
    8. EG3D
    9. A-NeRF
    10. HeadNeRF
    11. ICON
    12. DECA

    AI recommended 12 alternatives but never named Fictionarry/ER-NeRF. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • 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 Fictionarry/ER-NeRF?
    pass
    AI named Fictionarry/ER-NeRF explicitly

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

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

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

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MARKDOWN (README)
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  • Brand-free category queries5 vs 2 in Lite
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