Abstract. We present an adversarial network for rendering photorealistic hair as an alternative to conventional computer graphics pipelines.
Our deep learning approach does not require low-level parameter tuning nor ad-hoc asset design. Our method simply takes a strand-based
3D hair model as input and provides intuitive user-control for color and
lighting through reference images. To handle the diversity of hairstyles
and its appearance complexity, we disentangle hair structure, color, and
illumination properties using a sequential GAN architecture and a semisupervised training approach. We also introduce an intermediate edge
activation map to orientation field conversion step to ensure a successful
CG-to-photoreal transition, while preserving the hair structures of the
original input data. As we only require a feed-forward pass through the
network, our rendering performs in real-time. We demonstrate the synthesis of photorealistic hair images on a wide range of intricate hairstyles
and compare our technique with state-of-the-art hair rendering methods