A deep learning‐based framework for fast generation of photorealistic hair animations. Issue 2 (30th September 2022)

• Record Type: Journal Article
• Authors: Qiao, Zhi; Li, Tianxing; Hui, Li; Liu, Ruijun
• Is Part Of: IET image processing. Volume 17:Issue 2(2023)
• Page Start: 375
• Page End: 387
• Publication Date: 2022-09-30
• Subjects: Image processing -- Periodicals; 621.36705
• DOI: 10.1049/ipr2.12638
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract: Hair is the most important but onerous step for depicting dynamic 3D virtual characters. The photorealistic hair animation requires high‐quality simulation and rendering models. These models are based on complex calculations of mechanics and optics. Because of the huge time budget, it is difficult to apply in the interactive scene. A promising solution to overcome the time budget is the reduced model that struggles to reduce the computation of physical details by various interpolation methods. However, current reduced models compromise too much reality. This research intends to achieve photorealistic hair animation in a fast way. Building a deep learning‐based framework to synthesize photorealistic hair is aimed at. Furthermore, this research also presents a pipeline for hair merging into the scene. This new framework enables the model to significantly improve the appearances of hair animation while adding little computation overhead.