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

Japanese

最近のデバイスは複雑な計算パイプラインを使用して、多様な照明下で顔のレンダリングを改善します。これらのパイプラインは、メーカーによって広く異なりますので、同じ肖像画はスマートフォン間で顕著に異なります。これらの違いは、顔の外観の複数の側面に影響を及ぼし、感覚的な好みを単一の測定で特徴づけることが困難になります。これに対処するために、我々は、顔の対比を3つのコンポーネントに分解する解釈可能なフレームワークを提案します:全体的な顔のトンネルの分離、目と地域の対比、および細かい皮膚の詳細のマイクロコントラスト。我々は、それぞれのコンポーネントのための専用

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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