Researchers from Hugging Face have introduced Color Pass-Through, an end-to-end learned framework that addresses the persistent color mismatch between real-world scenes and their display on smartphone screens. The work, published as a research paper, tackles the systemic challenge of information loss that occurs when camera and display are calibrated independently.
Traditional pipelines factor the capture-to-display process into two separately calibrated stages connected by low-dimensional color transforms, leading to bottlenecks and error accumulation. Color Pass-Through instead treats the camera and display as a coupled system, learning the complete path end-to-end for each device.
The framework offers two key advantages: (1) end-to-end optimization brings entire real-world scenes to the display, and (2) a one-step calibration for each observer via the complete capture-to-display path. Validation using both digital and human observers showed an average gain of +2.0 points on a 5-point user study and more than 2x improvement on quantitative metrics compared to representative baselines.
Color Pass-Through learns pretrained neural components for a fixed camera-display pair, then uses a one-step calibration for each observer to deliver consistent color pass-through across diverse scenes. The paper is available on Hugging Face under the ID 2607.12746.