Efficient and Stable Generation of High-Resolution Hair and Fur With ConvNet Using Adaptive Strand Geometry Images

Abstract

This paper proposes a technique for transforming low-resolution (LR) simulations of hair and fur into high-resolution (HR) representations without noise, using strand geometry images in the form of lines and Convolutional Neural Network (CNN or ConvNet). LR and HR data for training are obtained through physics-based simulations, and LR-HR data pairs are set up. The training data involves converting the positions of hair strands into geometry images for use in the learning process. The proposed strand network in this paper is used as an image synthesizer to upscale LR images to HR images. When the obtained HR geometry image is transformed back into HR hair through the nonlinear transform proposed in this paper, it effectively captures the elastic movement of hair that is challenging to represent with a single mapping function alone. Furthermore, we extended the solver to accommodate hair structured in a triangular mesh form. We also propose a method for adaptively refining the hair particles of strands using the Bezier curves algorithm. Additionally, we improve the algorithm to ensure that the proposed strand geometry image can work effectively even in adaptively sampled hair particle structures. As a result, the neural network enables the stable generation of strands not only for straight and curly hair but also in various environments where different external forces are applied. Furthermore, the proposed research generates results faster than previous physics-based simulations and can be easily executed without requiring a deep understanding of complex numerical analysis processes.

Keywords

• Hair simulation
• fur simulation
• convolutional neural network
• physically based simulation
• strand geometry image
• adaptive hairs