RRepoGEO

REPOGEO REPORT · LITE

ashawkey/RAD-NeRF

Default branch main · commit 0de5ed25 · scanned 6/6/2026, 5:03:20 PM

GitHub: 928 stars · 157 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 ashawkey/RAD-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

2 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 to clearly state the project's application

    Why:

    CURRENT
    # RAD-NeRF: Real-time Neural Talking Portrait Synthesis

This repository contains a PyTorch re-implementation of the paper: Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition.
    COPY-PASTE FIX
    # RAD-NeRF: Real-time Neural Talking Portrait Synthesis

RAD-NeRF enables the real-time synthesis of realistic talking portrait videos from audio input, leveraging neural radiance fields and audio-spatial decomposition. This repository provides a PyTorch re-implementation of the paper.
  • mediumhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://ashawkey.github.io/rad-nerf/

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 ashawkey/RAD-NeRF
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepMotion
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepMotion · recommended 2×
  2. Synthesia · recommended 2×
  3. D-ID · recommended 1×
  4. NVIDIA Audio2Face · recommended 1×
  5. Rephrase.ai · recommended 1×
  • CATEGORY QUERY
    How can I synthesize realistic talking head videos from audio input in real-time?
    you: not recommended
    AI recommended (in order):
    1. DeepMotion
    2. Synthesia
    3. D-ID
    4. NVIDIA Audio2Face
    5. Rephrase.ai
    6. HeyGen
    7. SadTalker

    AI recommended 7 alternatives but never named ashawkey/RAD-NeRF. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best tools for generating 3D animated avatars that speak from audio?
    you: not recommended
    AI recommended (in order):
    1. Synthesia
    2. DeepMotion
    3. Ready Player Me
    4. Unreal Engine
    5. MetaHuman Animator
    6. Live Link Face
    7. Unity
    8. ARKit
    9. Reallusion Character Creator 4
    10. iClone 8
    11. Adobe Character Animator

    AI recommended 11 alternatives but never named ashawkey/RAD-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 ashawkey/RAD-NeRF?
    pass
    AI named ashawkey/RAD-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 ashawkey/RAD-NeRF in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ashawkey/RAD-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 ashawkey/RAD-NeRF solve, and who is the primary audience?
    pass
    AI named ashawkey/RAD-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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ashawkey/RAD-NeRF — 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