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Hair appearance is a complex phenomenon due to hair geometry and how thelight bounces on different hair fibers. For this reason, reproducing a specifichair color in a rendering environment is a challenging task that requiresmanual work and expert knowledge in computer graphics to tune the resultvisually. While current hair capture methods focus on hair shape estimationmany applications could benefit from an automated method for capturing theappearance of a physical hair sample, from augmented/virtual reality to hairdying development. Building on recent advances in inverse graphics and materialcapture using deep neural networks, we introduce a novel method for hair colordigitization. Our proposed pipeline allows capturing the color appearance of aphysical hair sample and renders synthetic images of hair with a similarappearance, simulating different hair styles and/or lighting environments.Since rendering realistic hair images requires path-tracing rendering, theconventional inverse graphics approach based on differentiable rendering isuntractable. Our method is based on the combination of a controlled imagingdevice, a path-tracing renderer, and an inverse graphics model based onself-supervised machine learning, which does not require to use differentiablerendering to be trained. We illustrate the performance of our hair digitizationmethod on both real and synthetic images and show that our approach canaccurately capture and render hair color.

Hair Color Digitization through Imaging and Deep Inverse Graphics