  Hair appearance is a complex phenomenon due to hair geometry and how the
light bounces on different hair fibers. For this reason, reproducing a specific
hair color in a rendering environment is a challenging task that requires
manual work and expert knowledge in computer graphics to tune the result
visually. While current hair capture methods focus on hair shape estimation
many applications could benefit from an automated method for capturing the
appearance of a physical hair sample, from augmented/virtual reality to hair
dying development. Building on recent advances in inverse graphics and material
capture using deep neural networks, we introduce a novel method for hair color
digitization. Our proposed pipeline allows capturing the color appearance of a
physical hair sample and renders synthetic images of hair with a similar
appearance, simulating different hair styles and/or lighting environments.
Since rendering realistic hair images requires path-tracing rendering, the
conventional inverse graphics approach based on differentiable rendering is
untractable. Our method is based on the combination of a controlled imaging
device, a path-tracing renderer, and an inverse graphics model based on
self-supervised machine learning, which does not require to use differentiable
rendering to be trained. We illustrate the performance of our hair digitization
method on both real and synthetic images and show that our approach can
accurately capture and render hair color.
