Color Pass-Through via Camera-Display Coupling

When a real-world scene is captured by a smartphone cam-era and viewed on its screen, the displayed image often differs noticeablyfrom the original scene in color, brightness, and contrast. This gap per-sists despite substantial advances in both modern cameras and displays.A key reason is that most pipelines factor the high-dimensional capture-to-display process into two separately calibrated camera and displaystages, and then connect them through low-dimensional color transforms,leading to information bottlenecks and inevitable error accumulation. Toaddress this systemic challenge, we propose Color Pass-Through, anend-to-end learned framework that operates directly on captured images.Our key insight is to treat the camera and display as a coupled systemrather than calibrating them in isolation. Coupling the camera and dis-play yields two practical advantages: (1) it brings the entire real-worldscenes to the display via end-to-end optimization, and (2) it allows effi-cient one-step calibration for each distinct observer via complete capture-to-display path. We validate Color Pass-Through using both digi-tal and human observers. Compared with representative baselines, ourmethod achieves an average gain of +2.0 points on a 5-point user-studyand more than 2× improvement on quantitative metrics, demonstratingimproved reproduction of the perceived color of the original scene. Seeproject page: https://lyricccco.github.io/color-pass-through/