Newest papers

Interpretable Face Contrast Modeling for Smartphone Portrait Rendering

Yujin Cho, Benoit Pochon, Jean‐Michel Morel, Gabriele Facciolo · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.26517 · cited by 0 works (OpenAlex) · open-access PDF

German

Die Qualität der zusätzlichen Smartphone-Porträtfotografie ist sowohl für Hersteller als auch für Nutzer zunehmend wichtig geworden. Jüngste Geräte verwenden komplexe Rechenleitungen, um die Gesichtswiedergabe unter unterschiedlicher Beleuchtung zu verbessern. Diese Pipelines unterscheiden sich stark zwischen Herstellern, so dass das gleiche Porträt über Smartphones deutlich unterschiedlich aussieht. Diese Unterschiede beeinflussen mehrere Aspekte des Gesichtsbildes und machen die Wahrnehmungspräferenz schwer mit einer einzigen Maßnahme zu charakterisieren. Um dies zu lösen, schlagen wir einen interpretierbaren Rahmen vor, der den Gesichtskontrast in drei Komponenten zersetzt: Gesamtgesamtgesamtgesamtgesamtgesamtgesamtgesamtgesamtgesamtgesamtgesamtgesamtgesamtgesamtgesamtgesamtgesamtges

Machine translation (Meta M2M100); the English original follows.

Abstract

The quality of smartphone portrait photography has become increasingly important to both manufacturers and users. Recent devices use complex computational pipelines to improve facial rendering under varied lighting. These pipelines vary widely across manufacturers, so the same portrait can look noticeably different across smartphones. These differences affect multiple aspects of facial appearance, making perceptual preference difficult to characterize with a single measure. To address this, we propose an interpretable framework that decomposes face contrast into three components: overall facial tonal separation, eye-region contrast, and micro-contrast of fine skin details. We formulate dedicated metrics for each component and validate them via pairwise human judgments, demonstrating that our approach correlates more closely with human contrast perception than traditional contrast measures. To support this analysis, we introduce a realistic mannequin-based dataset designed for systematic facial contrast evaluation, comprising 508 full-resolution captures from 29 smartphones under diverse lighting, with region-specific masks and extensive pairwise perceptual annotations. As a downstream analysis, we show that combining the three component-wise metrics provides a meaningful basis for modeling balanced facial contrast preference in a controlled setting, and we present a preliminary evaluation on a smaller real-portrait dataset indicating that insights derived from the mannequin study can extend to real images. Our work provides a controlled first step toward defining and evaluating face contrast in portrait imaging.

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Topics: Computer science, Rendering (computer graphics), Contrast (vision), Artificial intelligence, Pairwise comparison, Perception

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