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This commit is contained in:
Simon Lübeß
2025-12-04 20:06:34 +01:00
parent d246bb73e6
commit 5c7e6ab2c6
22 changed files with 993 additions and 16 deletions
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9
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2
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67
.obsidian/workspace.json vendored
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Attachments/Video_Augenlider_und_vertikale_Blickrichtung.mp4 Normal file
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Attachments/Video_Augenlider_und_vertikale_Blickrichtung_2.mp4 Normal file
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Bekerman et al. - 2014 - Variations in Eyeball Diameters of the Healthy Adults.pdf Normal file
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Drawing 2025-10-31 18.39.54.excalidraw.md Normal file
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---
excalidraw-plugin: parsed
tags: [excalidraw]
---
==⚠ Switch to EXCALIDRAW VIEW in the MORE OPTIONS menu of this document. ⚠== You can decompress Drawing data with the command palette: 'Decompress current Excalidraw file'. For more info check in plugin settings under 'Saving'
# Excalidraw Data
## Text Elements
Facial modeling and measurement based upon homologous topographical features ^9lhlkP3J
Modellierung ^dl5HVJd8
Definition der Parameter ^AgOMHbO0
Genome-wide mapping of global-to-local genetic effects on human facial shape ^c3JWt7PZ
mediolateral ^Ov3f7zgr
superinferior ^f4yNSv4W
anteroposterior ^ucgQyhdw
## Element Links
dl5HVJd8: [[Facial modeling and measurement based upon homologous topographical features]]
AgOMHbO0: [[Genome-wide mapping of global-to-local genetic effects on human facial shape]]
## Embedded Files
2872d153c44f6a1857f0cfab6737d320ced50851: [[Pasted Image 20251101190228_950.png]]
%%
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%%

43
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View File

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excalidraw-mask: true
excalidraw-export-dark: false
excalidraw-export-padding: 0
excalidraw-export-transparent: true
cssclasses: excalidraw-cropped-pdfpage
excalidraw-plugin: parsed
---
# Excalidraw Data
## Text Elements
## Element Links
fRxZZFNp: [[Bekerman et al. - 2014 - Variations in Eyeball Diameters of the Healthy Adults.pdf#page=3]]
CsrZqPvp: [[Bekerman et al. - 2014 - Variations in Eyeball Diameters of the Healthy Adults.pdf#page=3]]
## Embedded Files
cd001fa675a92254fff99e27c468bf3f57742526: [[Bekerman et al. - 2014 - Variations in Eyeball Diameters of the Healthy Adults.pdf#page=3]]
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```
%%

14
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@ -1,4 +1,4 @@
## 1. Umsetzung
## 1. Implementierung
- In Blender die Form des Modells variieren
- auch automatisiert (mit dem Batch-Script)
@ -13,13 +13,17 @@
- Recherchieren, wie man für gewöhnlich Gesichter / Augen modelliert (z.B. in Spielen und Filmen, also CGI allgemein)
- Verschiedene Ansätze raussuchen
- ggf. mehrere Ansätze ausprobieren, den besten Umsetzen
- Falls Parameter sich gegenseitig beeinflussen -> Sicherstellen, dass das Model jederzeit plausibel bleibt (z.B. kein Geometrie-Clipping)
## 2. Testen
- Am Ende Validieren -> Ist die Augenform überhaupt relevant für Erkennung der Pupille (ein kleinerer Teil der Arbeit als das Generieren selbst)
- TODO: Wie validieren
- Am Ende Validieren, ob die Augenform überhaupt relevant für Erkennung der Pupille ist
(ein kleinerer Teil der Arbeit als das Generieren selbst)
- Wie genau soll das validiert werden
- im Idealfall: nach jeder Iteration bzw. hinzugefügten/geänderten Option (z.B. Augapfel variieren, Lid variieren) testen
- Für jede Iteration validieren: Verbessert diese Änderung die Segmentierung -> behalten, verschlechtert diese Änderung die Segmentierung -> entfernen / anderen Ansatz suchen
# Zeitplan
- Oktober bis Mitte November (1,5 Monate): Recherche
- November bis Februar (ca 4 Monate): Umsetzung
- So Januar bis Mitte Februar (ca 1,5 Monat): Validieren
- November bis Februar (ca 3 Monate): Umsetzung
- Januar bis Mitte Februar (ca 1,5 Monat): Validieren
- Mitte Februar bis Ende: Schreiben
![[Zeitplan.excalidraw|1000]]

1
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Für das Modellieren wurde das LoopTools-Plugin verwendet

66
Notizen/16.10.2025.md Normal file
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- Verschiedene Kategorien -> verschiedene Gewichtung
- Programmierarbeiten
- Hauptsächtlich Programmieren/Entwickeln
- Experimentell -> haufen Plots
- Forschung -> Literaturrecherche
- Hybrid
- Mischung aus Programmieren und Experiment <-- Hier sind wir
- Einflussreiche Punkte
- Arbeitsweise, Systematik, Vorgehensweise
- Wurde mit Plan gearbeitet oder nicht
- Sage ich in Meetings, was ich mache / vorhabe (kleiner TEil)
- Implementierung
- Coding Standards
- Projektstruktur
- Dokumentation
- Installationsanleitung
- gute Readme
- Kann ich einfach Copy/Pasten und alles läuft
- Wo kann man Sachen ändern
- insgestamt sauber, kann jemand damit weiter arbeiten
- Experimente
- Wissenschaftliches Arbeiten
- Systematisches Vorgehen
- Gute Planung -> aufschreiben, dann ausführen
- gut durchdacht
- iterativ
- sollte ich noch ein Experiment durchführen
- gut, ausführlich, verständlich dokumentieren
- WIederholbarkeit, Reproduzierbarkeit
- Wieder readme
- feste seeds
- Grafische Darstellung
- Gute Plots
- gängige Darstellungen
- Alle Daten, die für Experimente braucht verfügbar machen
- Ergebnis:
- es geht __NICHT__ wie gut es funktioniert
- sondern wissenschaftlich:
- wenn es nicht geklappt hat
- woran liegt es
- Darstellung meiner Ergebnisse
- verkaufen: Leser denkt sich "Oh ja, dass sieht komplex aus, das ist ok, das es nicht funktionert hat"
- Schriftliche Arbeit:
- Schreibstil
- Rechtschreibung
- ist alles vollständig beschrieben
- kann man es anhand der Arbeit reproduzieren, validieren
- alles in Grundlagen beschrieben
- Aufbau
- Motivation
- Grundlagen
- Relevante Arbeiten
- Methodik / Implementierung / Experimente
- ggf. Fehleranalyse
- Future Works
- Fazit
- Zusammenfassung, Schlussfolgerung
Generell:
- so um die 100 Seiten
- soll nicht langweilig sein
- soll ausreichend gut erklärt sein -> damit man immer weiß, was abgeht
- ggf. Verweise zurück auf die Grundlagen
- gut lesbar für Fachfremde?
- lieber mehr beschreiben als zu wenig, aber nicht langweilig

1
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- Heuristik um festzustellen, ob kanten Entstehen wenn man Parameter schlecht

15
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- bei dem aktuellen Modell: aktuellster Stand auf einem Branch nicht main
- Related works so drei bis fünf
Mal gucken:
Normalerweise related works: was gibt es schon, dann daraus Forschungslücke identifizieren.
Jetzt, was ist anders an unserem Modell, warum ist ein neuer Ansatz nötig?
Server:
- Alles selbst installieren (treiber, conda, etc...)
- Natürlich hiter ienem Proxy: https://www.luis.uni-hannover.de/de/services/it-sicherheit/web-proxy/konfiguration#c12321
- Eigentlich nur in /etc/environment das hier:
```
http_proxy=[http://filter-proxy.rrzn.uni-hannover.de:3129](http://filter-proxy.rrzn.uni-hannover.de:3129) https_proxy=[http://filter-proxy.rrzn.uni-hannover.de:3129](http://filter-proxy.rrzn.uni-hannover.de:3129)
```

265
Paperung/Drawing 2025-11-30 22.08.28.excalidraw.md Normal file
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---
excalidraw-plugin: parsed
tags: [excalidraw]
---
==⚠ Switch to EXCALIDRAW VIEW in the MORE OPTIONS menu of this document. ⚠== You can decompress Drawing data with the command palette: 'Decompress current Excalidraw file'. For more info check in plugin settings under 'Saving'
# Excalidraw Data
## Text Elements
## Embedded Files
89b62fefd82d2e68ab50c26d7cbbc55215b68c70: [[Bekerman et al. - 2014 - Variations in Eyeball Diameters of the Healthy Adults.pdf#page=1]]
18ab74b156955ecf705b551f4992300b8f01017e: [[Bekerman et al. - 2014 - Variations in Eyeball Diameters of the Healthy Adults.pdf#page=2]]
cd001fa675a92254fff99e27c468bf3f57742526: [[Bekerman et al. - 2014 - Variations in Eyeball Diameters of the Healthy Adults.pdf#page=3]]
fd5943356d7e5513af53ec7505ce026beedec3bb: [[Bekerman et al. - 2014 - Variations in Eyeball Diameters of the Healthy Adults.pdf#page=4]]
ac01db8f592667d5e8e1d03c6f1515f4bc5b708d: [[Bekerman et al. - 2014 - Variations in Eyeball Diameters of the Healthy Adults.pdf#page=5]]
%%
## Drawing
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%%

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#rendering
Modellieren von Gesichtern mit Hilfe von morph shapes (Shape Keys in Blender).
Es wird ein (relativ niedrig auflösenden) Basis-Mesh eines Gesichts erstellt.
## Parametrisierung:
Basierend auf z.B. diesem Paper: [[Genome-wide mapping of global-to-local genetic effects on human facial shape]]
Okay, sie haben glaube ich andere benutzt... nochmal die Verweise auf Seite 6 angucken
Für jeden Parameter wird ein Shape Key erzeugt:
- Shape Keys können entweder von -1 bis 1 reichen (z.B. wie groß ist die Nase)
- Shape Keys können von 0 bis 1 reichen (z.B. ist ein bestimmtes Feature vorhanden oder nicht, bzw. wie ausgeprägt ist es)
Diese werden dann addiert (macht Blender alles, ist aber auch einfach lineares Blending).
- TODO: Angenommen wir möchten Augenlider damit simulieren: die müssten ja wohl nicht-linear sein, oder?
Für mehr Details: ggf. "Description-based visualisation of ethnic facial types"
Hinweis: die Features sind nicht unbedingt linear unabhängig: Seite 5 in diesem Paper verweist auf Quellen für ?Lösungen?
Die Parameter werden zwar von 0 bis 1 oder -1 bis 1 definiert, das sind Interpolationen zwischen Extrema, 0.5 hat z.B. keine statistische Signifikanz (Seite 7 hat das schöner geschrieben...)
## Koordinatensystem:
Koordinatenursprung ist der Mittelpunkt zwischen den vordersten Punkten der Hornhaut (Cornea Apex). Das funktioniert natürlich nur, wenn das Gesicht symmetrisch ist (was es für uns ruhig sein kann, wir interessieren uns eh nur für ein Auge).
![[Drawing 2025-10-31 18.39.54.excalidraw.md#^group=CDoKWtwZlBIES98P96vIb|Koordinatensystem im Kopf]]

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Paper: Variations in Eyeball Diameters of the Healthy Adults
CT Messungen von Augen von verschiedenen Ethnischen- und Altersgruppen sowie verschiedenen Geschlechtern.
## Tabelle mit Maßen für Augapfel und Augenhöle
![[Paperung/Drawing 2025-11-30 22.08.28.excalidraw.md#^area=E2s1rEDcxac3QtD5LzLqO|Eye Size Table|100%]]
Die Tabelle unterscheidet zwischen linkem und rechtem Auge. Das Paper sagt, der Unterschied ist ?___nicht___ statistisch signifikant. Also würde ich sagen, dass wir die Daten einfach zusammenfassen. Hinweis: Min und Max kann man einfach so kombinieren, aber die Standardabweichung und Mittelwert aber nicht (https://gemini.google.com/share/4fd4ee4bf50e) Interaktiver Rechner von Gemini, keine Ahnung, ob der richtig ist (wahrscheinlich nicht).
Geschlecht, Alter und ethnische Gruppe haben laut dieser Studie ___keinen___ statistisch Signifikanten Einfluss.
Die Breite des Augapfels hat einen Einfluss auf die Breite der Augenhöhle. Aber die anderen Durchmesser haben keinen Einfluss auf z.B. Höhe der Augenhöhle.

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aIst ein direktes Konkurrenz-Paper zu unserem.
Sie generieren mit Blender synthetische Augenbilder (und Segmentierungsmasken) für das Training von Eye-Trackern. (Cycles renderer)
Sie haben 24 3D-Modelle von Gesichtern gekauft (mit jeweils 8k Texturen):
\- 9 Männer + 9 Frauen für Diversität im Trainingsset
\- 3 Männer + 3 Frauen für Testset
__Interessante Info__: zum approximieren von Haut im Infraro-Bereich verwendne sie nur den Rot-Kanal der Diffuse-Textur.
Für Wimpern verwenden sie Blenders Haar-Partikel
## Modellierung des Auges
Für die Modellierung des Auges haben sie einige interessante Dinge implementiert:
1. __Augen-Modell mit Hornhaut:__ Sie haben ein 3D-Augapfel modelliert mit korrekter Form der Auswölbung der Hornhaut. Die Hornhaut hat einen physikalisch korrekten Lichtbrechungsfaktor
2. __Tränen Film:__ Sie haben einen transparente und spiegelnde Schicht um den Tränenfilm zu simulieren
3. __Augenlider:__
Augenlider werden mit Hilfe von Blenders Wrapping-Funktion an das Auge angeschmiegt.
Das Augenlid wird linear anhand der vertikalen Blickrichtung weiter geöffnet und geschlossen.
Beispiele:
![[Video_Augenlider_und_vertikale_Blickrichtung.mp4]]
![[Video_Augenlider_und_vertikale_Blickrichtung_2.mp4]]
Tatsächlich folgt das Augenlid im Normalfall ungefähr der vertikalen Blickrichtung!
1. __Öffnung der Pupille:__ Die Pupille kann geweitet und verengt werden
2. __Tränendrüse:__ Sie haben eine Tränendrüse modelliert, diese wird als Teil der Haut segmentiert, nicht des Auges
3. __Helle Pupille:__ Es wird anhand der *Beckmann-Verteilung* modelliert, dass die Pupille hell anstatt schwarz ist, wenn der Einfallwinkel des Lichts innerhalb etwa 2,25° von der optischen Achse steht.
4. __Environment Maps:__ Für die Simulation von Umgebungsreflektionen gibt es 25 EnvMaps. 16 indoor, 9 outdoor. Helligkeit wurde variiert. Bilder wurde entlang allen Achsen gedreht um in jedem Bild einzigartige Reflektionsmuster zu haben.
5. __Brillenmodell:__ Mehr gibt es hierzu nicht zu sagen
6. __Iris und Aderhaut texturen:__ 9 verschiedene Infrarotbilder von Iriden verwendet. Diese wurden für jedes Bild zufällig rotiert
Sie haben drei Datensätze generiert. Zwei immitieren die Daten in den NVGaze bzw. OpenEDS Datensätzen. Der Dritte immitiert die viele mögliche Bilder des Pupil Laps Core mobile eye tracker. Die hälfte der Bilder ist mit Brille
## Testen
Sie haben mit RIT-Nets und SegNets getestet, ob die Datensätze generalisierbare Modelle trainieren können.
Beide Netze wurden je mit den *synthethischen* NVGaze und OpenESD Daten trainiert sowie einer Kombination beider. Getestet wurden sie mit dem dritten sythetischen Datensatz sowie Bildern von NVGaze und OpenESD.
Natürlich wurde Image Argumentation verwendet.

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In diesem Kapitel werden die Grundlagen erklärt, die für das Verständnis dieser Arbeit nötig sind. Abschnitt []
## 2.1. Geometrische Grundlagen
In der vorliegenden Arbeit
###

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- Andere Rendering Methoden:
- Unity Eyes
- Synth Eyes
- NVGaze
- Swirski
- RIT-Eyes
- 3D geometric morphometrics
- Ist das ein Verfahren um variationen im Schädel zu messen?
- Specimen Space
- Blendshapes (bzw. Shape Keys in Blender) scheinen der Ansatz zu sein, den wir verwenden wollen. ("Facial modeling and measurement based upon homologous topographical features")
- Weitere Quellen: Da einzelne Shape Keys sich durchaus gegenseitig beeinflussen müssen ggf. Gegenmaßnahmen ergriffen werden (Siehe Seite 5 des Papers)
-